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Research Review

A review series covering new papers and articles in AI and agent systems. Each review verifies claims against the original tables and figures, assesses reliability including conflicts of interest, and closes with practical takeaways — answering "can we trust this, and what should we change?" rather than just summarizing. Published daily, automatically.

💬 Every review ends with an AI chat — ask the AI that has read the review and its verified numbers about interpretation, implications, related companies and stocks. Answers come with source links.

· arXiv preprint

Cut Tokens 87%, Double Accuracy: A Review of CORAL's Working-Memory Reset for Long-Horizon Agents

CORAL lets long-horizon LLM agents checkpoint their own progress and reset cluttered context. We cross-checked the paper's tables: 87.4% fewer input tokens than ReAct and more than double the GAIA accuracy.

  • CORAL on Qwen3-8B reaches a 42.8% GAIA average, more than doubling the same model's ReAct baseline (19.4%) while cutting input tokens by 87.4% (paper's Table 1 and Table 2).
  • On the hardest GAIA Level 3 questions, CORAL scores 19.8% against CK-Pro-8B's 8.3%, even though CK-Pro-8B has the higher overall average — a single average understates the method where it matters most.
  • Prompting-only CORAL (no training) gained 8.0 points over ReAct on BrowseComp (150 questions, 22.7%→30.7%), but on a weaker backbone (Qwen2.5-7B-Instruct) prompting-only actually lost 3.9 points, requiring SFT and RL to recover.
Cut Tokens 87%, Double Accuracy: A Review of CORAL's Working-Memory Reset for Long-Horizon Agents thumbnail

· arXiv preprint

Passed 82.8%, Caught Bugs in Only 33.8%: What Req2Test-Bench Reveals About Agent-Generated Tests

Letting agents write tests purely from requirements produces many passing tests but far fewer that actually reproduce real bugs. Req2Test-Bench tested eight agent-model configurations to show the gap.

  • The best configuration (Codex CLI + DeepSeek V4.1 Flash, high reasoning) passed 82.8% of target-hidden instances, but was kill-complete -- reproducing every mutant the developer's own test kills -- on only 33.8%.
  • Cost varied 30-fold ($0.039-$1.16 per generation) without tracking effectiveness; only three configurations (Codex/Luna, Claude Code/DS, Claude Code/Sonnet) sat on the cost-effectiveness Pareto frontier.
  • 90-99% of behaviorally inadequate tests stemmed from an 'information gap' where requirements left setup or expected outcomes implicit, and one added prompt instruction to use project fixtures raised KillComplete@1 by up to 15.0 points.
Passed 82.8%, Caught Bugs in Only 33.8%: What Req2Test-Bench Reveals About Agent-Generated Tests thumbnail

· arXiv preprint (워크숍 트랙)

Significant in Only One of Three Domains: Reviewing Netflix's AT-AT Agent Tool Optimizer

Netflix introduces AT-AT, which rewrites tool descriptions and implementations offline from historical agent traces. Only one of three tau2-bench-verified domains showed a statistically significant gain, and only one of three production MCP servers saw task completion improve.

  • AT-AT edits tool descriptions and implementations offline from historical agent traces alone. On tau2-bench-verified Telecom it raised reward from 29.50% to 36.75% (+7.25pp, p=0.0033) — the only statistically significant gain among the three domains tested.
  • Airline (+7.00pp, p=0.2305) and Retail (−2.33pp, p=0.2313) were not statistically significant, and task completion improved on only one of three Netflix production MCP servers (Eng. Context, 54.5%→72.7%).
  • All five authors are Netflix employees and the production evaluation targets its own internal systems — a clear conflict of interest — and this is an unreviewed workshop-track submission.
Significant in Only One of Three Domains: Reviewing Netflix's AT-AT Agent Tool Optimizer thumbnail

· arXiv preprint

25%, 50%, 60% — Baselines Undisclosed: Reviewing ANASSA, a Supply-Chain Spatial Agent

We cross-checked the founder-authored ANASSA paper's 3-month PoC claims of 25% better data quality, 50% faster runtime, and 60% lower decision latency, but the absolute baselines stay confidential.

  • ANASSA specifies eleven components, a six-step cognitive loop, and eight typed data contracts that declare preconditions at every agent boundary, plus two blocking human-in-the-loop approval gates
  • A three-month PoC reported 25% better data quality, 50% lower workflow runtime, and 60% lower decision latency versus the prior manual workflow — but absolute baselines and sample sizes are undisclosed
  • The corresponding author is affiliated with the company commercializing ANASSA; the authors themselves call the results 'uncontrolled deployment observations' and admit gate dwell time, the key RQ4 evidence, was never captured
25%, 50%, 60% — Baselines Undisclosed: Reviewing ANASSA, a Supply-Chain Spatial Agent thumbnail

· arXiv preprint

A 60% Score Drop That Scale Can't Fix: Reviewing MCP-Injector's Fault-Tolerance Study

A 1,350-cell study injecting real MCP ecosystem failure patterns via MCP-Injector finds task-completion scores fall 60%, and the 120B model completes fewer tasks than the 20B one under faults.

  • Injecting failure distributions mined from 57,240 MCP issue/PR threads drops task-completion score from 8.08 to 3.23 (-60%).
  • Scaling from 20B to 120B buys no fault tolerance -- completion rate is actually lower (95.6% vs 91.1%, a 4.4-point gap, statistically uncertain).
  • Which agent stack (MCPBench, OpenAI SDK, LangChain) handles the fault matters more than model size.
A 60% Score Drop That Scale Can't Fix: Reviewing MCP-Injector's Fault-Tolerance Study thumbnail

· arXiv preprint

4.7 Points Ahead of the Strongest Rival: Reviewing RoleFlow's Self-Evolving Multi-Agent Structure

Researchers from Maryland and partners show RoleFlow, which edits its multi-agent collaboration structure online via SECPO training, beating the strongest baseline W4S by 4.7 points averaged across seven benchmarks.

  • Averages 67.1 across seven benchmarks, 4.7 points ahead of the strongest rival W4S at 62.4 (we re-verified both averages arithmetically)
  • Removing the role library collapses MuSiQue from 42.1 to 16.3 — synthesizing new specialist roles matters more than deciding when to edit
  • SECPO costs 48% more training compute than GRPO (9.1→13.5 chain executions per question) but buys 2.1–2.6 points of accuracy
4.7 Points Ahead of the Strongest Rival: Reviewing RoleFlow's Self-Evolving Multi-Agent Structure thumbnail

· arXiv preprint

Top Score Just 50.6%: Reviewing PLE, the AI-Agent Benchmark for Physical Engineering

Google Cloud AI Research's 85-task benchmark for real electrical, civil, structural and mechanical engineering work found the best of 86 model-harness pairs scored only 50.6%, from Claude Opus 5.5.

  • The best configuration (Claude Opus 5.5 in Claude Code) averaged only 50.6%, spending 27M tokens and 63 minutes per task
  • The gap between models dwarfs the gap between harnesses -- models span 50.6% to 14.2%, while harnesses differ by a median of just 3.0 points
  • The LLM judge shares a lineage with the top model (Claude Opus 5.5), and the authors self-disclose and re-verify a 2.3-point self-preference bias
Top Score Just 50.6%: Reviewing PLE, the AI-Agent Benchmark for Physical Engineering thumbnail

· arXiv preprint

ASR 27%→5%, Utility Loss Just 1.6pp: Reviewing MAS-Guard's Multi-Agent Guardrails

Oracle AI's unified guardrail MAS-Guard cuts attack success rate 71.5-86.3% across six benchmarks and three LLMs while barely touching benign-task utility.

  • ASR falls from 27.1% to 4.8% for GPT-5.6-Terra (-82.3%); the cut ranges 71.5-86.3% across all three backbones
  • Rival guardrail FIDES posts lower ASR on ACIArena and AgentLeak but fails the overall utility criterion for every backbone — a concrete safety/utility trade-off
  • Ablation shows local A-Guard alone cuts ASR from 27.1% to 7.8%, while deterministic system checks alone barely move it (27.1%→26.4%) — local checks are the dominant lever
ASR 27%→5%, Utility Loss Just 1.6pp: Reviewing MAS-Guard's Multi-Agent Guardrails thumbnail

· arXiv preprint (EMNLP 2026 Industry Track)

Latency 71s to 12s, Quality Unchanged: Reviewing DiffResearch

A controlled backbone swap from autoregressive to diffusion cuts a multi-agent deep-research pipeline's latency 6x on Deep Research Bench, with RACE quality scores unchanged.

  • 6x faster end-to-end (71.45s to 11.89s) by swapping only the backbone to diffusion, holding the agent scaffold fixed.
  • RACE quality scores stay flat (0.3895-0.3969) across AR, dFull, and dFast -- speed doesn't cost quality in this controlled test.
  • A manual grounding audit found only 28% of claims fully supported by retrieved evidence -- a retrieval-channel caveat, not a decoding one.
Latency 71s to 12s, Quality Unchanged: Reviewing DiffResearch thumbnail

· arXiv preprint (Salesforce AI Research)

Top Overall, 5th on Safety: Reviewing EGI-Bench's 14-Model Enterprise Agent Evaluation

EGI-Bench unifies eight enterprise agent benchmarks. The overall leader, gpt-5.5, ranks only 5th on the safety axis among fourteen models.

  • gpt-5.5 leads overall EGI at 65.61, but its safety/abstention score (M5, 63.47) trails Claude Sonnet 4.5 (80.28) by 17 points, ranking only 5th of fourteen
  • Claude Haiku 4.5, 6th overall, posts the single best engineering-quality score (M6, 61.28) in the roster -- a reversal overall rank alone hides
  • Salesforce AI Research both designed and weighted the framework and authored several of its eight contributing benchmarks (CRMArena, HERB, Benchforce), a clear conflict of interest
Top Overall, 5th on Safety: Reviewing EGI-Bench's 14-Model Enterprise Agent Evaluation thumbnail

· arXiv preprint

Tool Calls Underperform Compaction: Reviewing Native Context Editing (NCE)

NCE embeds context editing in the model's own vocabulary, cutting tokens 36% and raising success 5.6 points over harness compaction. A tool-call baseline trained on the identical edits scored below plain compaction, showing the gain comes from how edits are expressed.

  • A tool-call baseline trained on the identical edits averages 47.3% success, below plain harness compaction's 48.3%, and saves only 6% of tokens, while NCE saves 36%, showing most of the gain comes from native expression, not edit content (Table 1)
  • Shrinking the budget from 256K to 48K on Terminal-Bench 2.0, NCE keeps its lead over compaction at +5.6 to +10.1 points with 26-39% fewer tokens at every setting (Table 2)
  • A quantitative audit of the training-data pipeline found 80.0% of trajectories defective under a single teacher and 100.0% under original-history labeling; NCE's two-teacher, evolving-context design lowers this to 37.5%
Tool Calls Underperform Compaction: Reviewing Native Context Editing (NCE) thumbnail

· arXiv preprint

Capability Gap Closes, Security Gap Doesn't: Reviewing GatewayBench on MCP Gateways

GatewayBench benchmarks 7 MCP gateways and finds that upgrading the model closes the task-success gap between architectures but not the indirect-attack-success gap.

  • With no noisy tools injected, mean indirect attack success is 19.6-30.9% for hierarchical gateways versus 42.5-60.5% for flattened ones across all three model/harness combos (Table 2)
  • Upgrading to GPT-5.4 closes the task-success gap (87.2% vs. 85.1%) but not the indirect-attack-success gap (47.2% vs. 30.9%) -- capability and security don't improve together
  • Injecting 1,500 noisy tools pushes flattened-gateway tokens per turn from 8.3k to 233.3k (about 28x), while hierarchical gateways stay nearly flat at 9.1k to 12.6k (GPT-5.4)
Capability Gap Closes, Security Gap Doesn't: Reviewing GatewayBench on MCP Gateways thumbnail

· arXiv preprint

Medal Rate 37.9%→72.7%: Reviewing MLExpert, an Autonomous ML Engineering Agent

MLExpert, a 35B agentic model, lifts its MLE-Bench Lite medal rate from 37.9% to 72.7% — matching larger frontier models. We cross-checked the staged SFT to RL training recipe against the paper's tables.

  • MLExpert raises the Any-Medal rate on MLE-Bench Lite's 22 tasks from 37.9% (base Qwen3.6-35B-A3B) to 72.7%, the best among same-scale models and on par with larger models like GLM-5.3 and Kimi-K3.
  • A stage-by-stage breakdown (Table 3) shows each training phase adding independently: General Agent SFT 37.9%→47.0%, MLE-domain SFT 47.0%→62.1%, and Windowed Interaction RL (WIRL) 62.1%→72.7%.
  • Starting from the same MLE-SFT checkpoint, WIRL (72.7%) beats GRPO (56.1%) and PPO-style optimization (60.6%); removing either the trend reward or policy correction alone drops it to 65-67%, showing both design choices matter (Table 4).
Medal Rate 37.9%→72.7%: Reviewing MLExpert, an Autonomous ML Engineering Agent thumbnail

· arXiv preprint

Accuracy 62.1%→84.9%, False Consensus 40.7%→17.0%: Reviewing ECMR's Deliberation Controller

Agreement in a multi-agent LLM system doesn't mean correctness. ECMR separates answer concentration from evidence independence, lifting accuracy by 22.8 points and cutting false-consensus rate by more than half versus a fixed debate protocol.

  • Across 9,000 held-out MedQA/CaseHOLD/FinQA records, ECMR-Full raises accuracy from 62.1% to 84.9%, cuts false-consensus rate (FCR) from 40.7% to 17.0%, and raises evidence-support rate (ESR) from 58.0% to 88.7% — all three move together, not as a trade-off
  • Shuffling the controller's input state while keeping the same actions and roles cuts only 0.7 rounds yet costs 11.7 accuracy points — the gain comes from mapping evidence state to action, not from more deliberation
  • The adaptive edge is only 1.3 points on easy cases but widens to 37.1 points on hard ones — the extra computation is spent selectively, on cases where evidence genuinely remains unresolved
Accuracy 62.1%→84.9%, False Consensus 40.7%→17.0%: Reviewing ECMR's Deliberation Controller thumbnail

· arXiv preprint

Local Attack Success 90%, Consequence Conversion 7-100%: Reviewing Agent-Canary-Mas

A controlled study of 975 matched attack pairs across A2A, ANP and BCS multi-agent protocols finds local attack success rates around 90%, but how often that actually converts into a real task consequence swings from 7.1% to 100% depending on the risk class.

  • Local attack success (ASR) averages 72-100% across the three protocols, but consequence conversion (PTCR) swings from 7.1% (ANP-T4) to 100.0% (BCS-T3) by risk class — a security metric that reports ASR alone both over- and under-states real risk
  • Attribution using only a protocol's default logs (Native View) scores just 29.7% Audit-F1; adding cross-node evidence linking selection, delivery, consumption and effect lifts it to 95.5% (+65.8 points)
  • A protocol-context Guard that re-checks authority at every handoff cuts average Finance-task ASR from 86.1% to 53.8% and PTCR from 65.5% to 31.2% — but task completion also drops from 99.8% to 95.1%, a real cost, not a free lunch
Local Attack Success 90%, Consequence Conversion 7-100%: Reviewing Agent-Canary-Mas thumbnail

· arXiv preprint

43.6% Task Completion, 19.0% Full Compliance: Coding Agents' Hidden Non-Compliance — ANCHOR Review

Agents get the fix right but break the rules on scope, order, and reporting — we checked the gap between task completion and constraint compliance against the source tables.

  • The best configuration, Claude Opus 4.8 under Cline, completes 43.6% of tasks but fully passes every checklist item (FCCR) on only 19.0% — 81.0% of tasks break at least one rule
  • Swapping only the scaffold for the same model (GPT-5.3-Codex) moves completion by just 0.3 points but IPR by 5.0 and FCCR by 3.1 — a completion-only dashboard misses this risk entirely
  • Reporting compliance is the lowest of all categories at 41.0% — disclosing what the agent actually did is the rule most often broken
43.6% Task Completion, 19.0% Full Compliance: Coding Agents' Hidden Non-Compliance — ANCHOR Review thumbnail

· arXiv preprint

Detection 47.7%→71.9%, -25.5pp In-House: Reviewing CRBugBench on Code Review Agents

Review agents' bug-catching rate jumped from 47.7% to 71.9% in five months, but drops up to 25.5 points on large proprietary repos — we checked the source tables.

  • claude-opus-5 catches 71.9% of bugs on average within its top-5 PR review comments — up sharply from opus-4.6's 47.7% five months earlier
  • On large proprietary repos (100k+ files on average), the same agents drop up to 25.5 points — only 22.8% of bugs are visible directly in the diff there, versus 46.1% on GitHub
  • Combining human and agent reviews catches 82.1% of bugs (vs. 50% humans alone, 71.9% agents alone) — evidence for collaboration design over full automation
Detection 47.7%→71.9%, -25.5pp In-House: Reviewing CRBugBench on Code Review Agents thumbnail

· arXiv preprint

Completion 75.8%→53.5%: The MCP Paradox in a Baseline Assessment of the Agentic Web Stack

An empirical study ran 10,800 task executions to test how MCP and A2A protocols change LLM agent task completion rate — we checked the tables against the source.

  • Model, protocol, and framework all differed significantly (Kruskal-Wallis, all p<0.001) — Qwen2.5-7B's completion rate dropped most, from 75.8% under standard calls to 53.4% under MCP
  • MCP raised tool-use success to its highest value (68.1%) while lowering completion rate to its lowest for every model — a two-sided effect
  • The paper's own 'best stack per metric' table ranks by factor-level mean rank, a different question from the single highest raw cell (79.5%, Qwen + standard + LangChain) — conflating the two misleads
Completion 75.8%→53.5%: The MCP Paradox in a Baseline Assessment of the Agentic Web Stack thumbnail

· arXiv preprint

63.6% to 100% on Structured Questions: Reviewing UniDataAgent's Ontology-Grounded Enterprise Agent

UniDataAgent grounds enterprise question answering in an ontology, and this review verifies how and how much it closes the gap with document RAG on completeness and cross-metric questions.

  • 95.0% (UniDataAgent) vs 72.5% (Doc-RAG) overall across 40 questions — on completeness-demanding types Doc-RAG fell to 57.1-63.6% while both types hit 100% for UniDataAgent
  • Per-type figures, weighted, reproduce the overall numbers exactly — no sign of aggregation error or selective reporting
  • Evaluation is 40 questions, one baseline, with a clear author conflict of interest — replication in your own environment is needed before adoption
63.6% to 100% on Structured Questions: Reviewing UniDataAgent's Ontology-Grounded Enterprise Agent thumbnail

· arXiv preprint

The Sub-Agent That Interrupts Wins: POrchestra Lifts GAIA2 from 44.5% to 55.5% at 36% Lower Cost

A framework letting sub-agents request mid-task adjustments beats fixed-harness orchestration on GAIA2 — higher success at lower per-task API cost, checked against the paper's own tables.

  • GAIA2 success (DeepSeek-V4-Flash) rises from 44.5% to 55.5% (+11.0pp, +24.6% relative) while per-task API cost drops 36%
  • Prior baseline AOrchestra beats single-agent ReAct on static GAIA but loses to it on dynamic GAIA2
  • Adding OPOD training to a small Qwen3.5-9B orchestrator lifts GAIA to 77.9% and GAIA2 to 49.2%, +5.5pp/+5.4pp over SFT alone
The Sub-Agent That Interrupts Wins: POrchestra Lifts GAIA2 from 44.5% to 55.5% at 36% Lower Cost thumbnail

· arXiv preprint

Formulation Works, Code Breaks: How an MCP Documentation Server Nearly Quadrupled a Scheduling Agent's Success Rate

General-purpose LLMs orchestrated as agents can formulate constraint-programming scheduling models reliably, but implementation is the real barrier. An MCP documentation server plus a formulate/code split raised the executable-script rate from 14.8% to 59.3%.

  • Formulation+coding accuracy rises 14.8%→59.3% (multi-agent+MCP), reaching 80.6% on the four easier problems
  • Formulation ability (77-78%) is nearly workflow-independent -- the bottleneck is implementation
  • On resource-constrained warehouse problems (2,000+ constraints), formulation accuracy itself collapses 44.4%->14.8%
Formulation Works, Code Breaks: How an MCP Documentation Server Nearly Quadrupled a Scheduling Agent's Success Rate thumbnail

· arXiv preprint

Small Models Can Read a Plant Too: Reviewing the Ontology Tower, a Narrow-and-Deep Ontology for Equipment Agents

A preregistered evaluation on a real air-handling plant by the Korea Institute of Energy Research finds that the form of knowledge and where lessons are kept matter as much as model size.

  • Projected text lifts trap avoidance 0.605→0.802; a 9B model beats a ~750B model without it
  • Docs behind search score lowest (0.597); journal-only lessons go unused
  • 12 of 14 live runs succeed, yet at least 4 report setpoint changes never made
Small Models Can Read a Plant Too: Reviewing the Ontology Tower, a Narrow-and-Deep Ontology for Equipment Agents thumbnail

· arXiv preprint

Heuristic 97% vs Best Model 49%: A Review of StoreBench's Live-Commerce Operator-Agent Benchmark

Testing 7 frontier LLMs on a simulated apparel store, the best model (DeepSeek-V4-Pro) only reached 49% against a scripted heuristic's 97% pass rate, and trailed human experts (0.708) narrowly at 0.700.

  • Even the best model, DeepSeek-V4-Pro, passed only 49% of cells against the scripted heuristic's (smart-triage) 97%, with its composite score (0.700) edged out by human experts (0.708).
  • Switching to the Claude Code harness on the 365-day marathon sent four models (DeepSeek, Qwen, two Claude variants) above 0.96 — proof that harness choice itself is a major variable.
  • GRPO post-training Qwen3.5-27B on just five tasks raised its held-out composite from 0.136 to 0.373, and the share of profitable episodes jumped from 3% to 72%.
Heuristic 97% vs Best Model 49%: A Review of StoreBench's Live-Commerce Operator-Agent Benchmark thumbnail

· arXiv preprint

Developer-Facing MCP Error Text Stops the Newest Agents: Reviewing the 45%→84% Recovery Experiment

We verify a paper that surveyed 3,001 error messages in 150 popular MCP servers and ran 15,120 trials on five OpenAI models, showing one human-facing sentence can block agent recovery.

  • One terminal-command sentence cuts expired-credential recovery 82%→45%; GPT-6 Astra loses 69 points
  • Naming a server tool restores 84%; rate-limit recovery jumps 6%→88%
  • 62 of 67 credential-error steps ask for something the agent can't do
Developer-Facing MCP Error Text Stops the Newest Agents: Reviewing the 45%→84% Recovery Experiment thumbnail

· arXiv preprint

Balanced Accuracy 80.9%, Side-Effect Recall Just 24%: A Reliability Review of Long-Horizon Computer-Use Agent Judges

A paired-trajectory benchmark of 1,373 human-recorded tasks tested 11 LLM judges — the best (GPT-5.5) hit only 80.9% balanced accuracy, and recall for catching "succeeded but caused a bad side effect" topped out at 24.1%, with most judges scoring in the single digits to low teens.

  • GPT-5.5 with the Codex harness topped 11 agentic/direct judges at 80.9% balanced accuracy, but its recall for Bad Side Effect negatives — trajectories that acted on the wrong device or account — was only 24.1%, the best of any judge; most scored under 10%.
  • The same model swung widely by harness: GPT-5.5 gained 12.3 points moving from direct judgment (68.6%) to the Codex harness (80.9%), while Qwen3.6 27B lost 7.2 points moving from direct judgment (77.6%) to the OpenCode harness (70.4%).
  • Removing screenshots and leaving only the text action log dropped balanced accuracy to 57.7%; removing the instruction itself (a provably unsolvable control) dropped it to near-random 47.8-49.8%, showing visual evidence carries most of the judging signal.
Balanced Accuracy 80.9%, Side-Effect Recall Just 24%: A Reliability Review of Long-Horizon Computer-Use Agent Judges thumbnail

· arXiv preprint

Give Up 1.4 Points, Cut Cost 82%: Per-Call-Site Model Routing Review

Routing nine models by call site matches a frontier model at 4 of 5 sites, cutting cost 82%.

  • Pairwise McNemar tests across nine models (0.8B to frontier) on the same 280 cases found the best local model (Gemma4 26B) statistically indistinguishable from both hosted models at four of five call sites — only code generation showed a significant gap (p=0.002 vs. Claude Sonnet 5).
  • Routing every call site to its best local model reaches 91.8% accuracy (vs. 95.4% for Sonnet 5 alone) at zero per-call cost; hosting only the two generative sites reaches 93.9% (-1.4pp) while cutting spend 82% (from $12.13 to $2.15).
  • A live-deployment comparison (43 matched cases) reproduced the same trade-off — 39/43 successes at 28% of the cost ($0.70 vs. $2.47) — but aggregate accuracy hid a safety failure: one small model (Gemma4 E2B) issued service calls on 87.2% of requests for devices the site doesn't own.
Give Up 1.4 Points, Cut Cost 82%: Per-Call-Site Model Routing Review thumbnail

· arXiv preprint

Teaching Agents to Quit: Reviewing HERA's Harness-Environment Co-Evolution

HERA lifts held-out abstention accuracy from 61.7% to 83.3% via harness-environment co-evolution.

  • On the held-out hera-bench set, swapping only the harness on the same base model raises Abstain from 61.7% to 83.3%, Act from 68.3% to 76.7%, and Pair (both correct) from 48.3% to 70.0%.
  • Applying the harness built on GPT-5.6-Luna to 19 other models lifts Abstain by +15.3pp and Pair by +12.5pp on average (range +6.7 to +40.0pp Abstain), but weaker models like Mistral Large 3 saw over-abstention rise from 13.3% to 15.0%.
  • Across 14 models, HERA raises median API cost 2.09x and tokens 3.31x, yet GPT-5.6-Luna+HERA matches base-harness GPT-6-Astra's 70.0% paired success at about 85% lower cost per episode ($0.0091 vs $0.0610).
Teaching Agents to Quit: Reviewing HERA's Harness-Environment Co-Evolution thumbnail

· arXiv preprint

Attack Success Rate 64.4%: COPEX Splits Model Blame From Lower-Layer MCP Compromise

Across nine models and 3,375 trials, COPEX found a 64.4% mean attack success rate on MCP agents. Combined scanning cut it to 41.1% — but MCP rebinding stayed at 100%.

  • Across nine models and 3,375 trials, COPEX measured a mean attack success rate (ASR) of 64.4%, with surface-level means from 58.3% (model/agent) to 71.4% (transport) — attacks that never touch the user prompt succeeded just as often or more.
  • Combined input+context scanning cut mean ASR from 81.6% to 41.1% (a 49.6% relative drop) on an 8-attack defense subset, but MCP rebinding — which operates below the model's observation boundary — stayed at 100% ASR under all four defense configurations.
  • Switching the outcome oracle from deterministic trace checks to an LLM judge raised the same six attacks' mean ASR from 77.1% to 83.3% (+6.3pp), and swapping ReAct for Reflexion moved mean ASR from 86.2% to 82.5% (-3.7pp) with model-dependent direction (Sonnet-4.6 -9.9pp vs Grok-4.3 +0.5pp).
Attack Success Rate 64.4%: COPEX Splits Model Blame From Lower-Layer MCP Compromise thumbnail

· arXiv preprint

Multi-Agent Systems Burn 6.36x the Energy: A Sustainability Review of Agentic LLM Dev Tools

Across five SE tasks (code gen, tech debt, vulnerability detection, log parsing, log analysis), four-agent setups used 6.36x the energy and 6.07x the latency of non-agentic baselines, but improved accuracy on only one task.

  • Versus non-agentic (NA), single-agent (SA) used 1.20x energy / 1.23x latency, dual-agent (DA) 3.06x / 3.32x, and four-agent (MA) 6.36x / 6.07x (averaged over 5 tasks, 6 models, 3 hardware platforms).
  • Accuracy gains were task-specific — MA's 55.4% F1 beat NA (49.7%), SA (49.2%), and DA (35.6%) on vulnerability detection, but log-parsing exact-match fell from 34.5% (NA) to 26.1% (MA) as more agents were added.
  • Of 66 three-objective (accuracy/energy/latency) Pareto-optimal configurations across 15 task-hardware pairs, 59 (89%) were non-agentic or single-agent, and only 1 was four-agent.
Multi-Agent Systems Burn 6.36x the Energy: A Sustainability Review of Agentic LLM Dev Tools thumbnail

· arXiv preprint

One Kept Candidate Flipped −8.6pp: Reviewing SELF-POT on Test-Time Scaling

We check whether test-time compute lets cheap models match expensive ones, against a controlled cost-accuracy benchmark across 5 models and 4 domains.

  • Coding Parallel@4 correct submissions were 376/500 scheduled cells (−8.6pp), but keeping the first valid candidate on selection failure alone raised it to 437 (+3.6pp), and public-example selection to 453 (+6.8pp) — saving 12–49% of cost per model.
  • The same five low-cost models under the same budget multiple moved in opposite directions by domain — math Parallel@4 averaged +4.0pp (95% CI [+0.3,+7.7]), coding −8.6pp ([−11.8,−5.4]), workflow −4.6pp ([−7.4,−1.9]).
  • On workflow and terminal tasks, Claude Opus 5.5's single Direct answer beat every low-cost model under every test-time protocol (workflow 148/160 vs. best 78/160; terminal 13/30 vs. best 3/30).
One Kept Candidate Flipped −8.6pp: Reviewing SELF-POT on Test-Time Scaling thumbnail

· arXiv preprint

The Upgrade Wasn't Just Capability: Reviewing SEABench on Self-Evolving Agent Safety

Letting an agent rewrite its own harness raised completion from 35.7% to 47.2% - but safety failures rose right along with it, from 0% to 43.9%.

  • In paired same-model comparisons, the safety failure rate jumped from 0% (0/720) before self-evolution to 43.9% (316/720) after, with a Fisher's exact test p=3.48×10⁻¹³.
  • By evolution surface, tools/skills was most vulnerable at 55.8% (134/240), ahead of controller at 38.8% (93/240) and memory at 37.1% (89/240) - all three breakdown axes summed to the same 316 failures.
  • Chain-of-thought monitoring blocked 70.9% of harmful outputs (56/79) at a 9.7% false-positive rate (11/113), but its effectiveness dropped to 54.8% on the tools/skills surface.
The Upgrade Wasn't Just Capability: Reviewing SEABench on Self-Evolving Agent Safety thumbnail

· arXiv preprint

Right Fix, Still Fails 41% of the Time: The Incident-Arena Agent Reliability Review

Incident-Arena, a benchmark for production incident-response agents, shows that even issuing the complete reference fix still fails 41% of the time. The leading failure modes: repairs that don't survive a restart, and agents declaring done before recovery actually completed.

  • Across 20 real production tasks and 3,000 trials, 672 of 1,638 episodes (41%) that issued the complete reference fix still failed — knowing the fix doesn't guarantee a pass.
  • 26.5% of failures came from repairs that didn't survive a restart or the next trigger, and 23.9% came from agents declaring done before the service had actually recovered.
  • Raising reasoning effort from lowest to highest moved pass rate only 39.2%→43.8% (+4.6pp) while cost rose 2.3x — a weak lever relative to its price.
Right Fix, Still Fails 41% of the Time: The Incident-Arena Agent Reliability Review thumbnail

· arXiv preprint

Same Budget, Different Control: ProgramBench 63.7%→71.5% in Meta's Meta-Reasoning Review

Holding workers and call budget fixed, Meta's meta-reasoning controller lifted ProgramBench from 63.7% to 71.5% — but trailed direct control at low budgets.

  • On ProgramBench with GPT-5.5, swapping direct control for the meta-reasoning controller (same workers, same 1200-call budget) raised accuracy from 63.7% to 71.5% (+7.8 points); the self-run comparison to OpenAI's Codex (58.0%) and Anthropic's Claude Code (65.5%) is not an independent replication.
  • Averaged across three frontier models, meta-reasoning beat direct control by 4.0 points on IMO ProofBench-Advanced, 4.2 on ARC-AGI-2, and 3.6 on LongCoT-mini — but at a 400-call budget on ProgramBench with Opus 4.8 it trailed direct control, 56.6% vs 62.7%, before pulling ahead at 1200 calls (67.2% vs 65.3%).
  • The controller's own quality judgments beat worker self-confidence as a correctness signal (Type-2 AUC 0.88 vs 0.55 for Gemini 3.1 Pro on IMO ProofBench-Advanced), while its persisted state stayed in the thousands of characters versus over a million for direct control's full history.
Same Budget, Different Control: ProgramBench 63.7%→71.5% in Meta's Meta-Reasoning Review thumbnail

· arXiv preprint

Catches the Spy, Misses the Cabal: ORBIT Exposes a Blind Spot in Multi-Agent Defenses

The same defenses cut a compromised agent's attack success from 72% to 12%, yet left colluding agents' 30% success rate completely unchanged.

  • On SWE-Bench multi-issue tasks, the LLM monitor and guardian defenses cut a compromised agent's attack success from 72.0% to 12.0% (-60pp), yet the same defenses left collusion attack success (30.0%->30.0%/34.0%) essentially unchanged.
  • None of the four defenses (system prompt, LLM monitor, guardian, dual-LLM) generalized across all threat types, and dual-LLM cost 9.2 points of SWE-Bench multi-issue task completion.
  • Moving the same model (Qwen3-235B) from standalone to a Star topology raised undefended attack success from 17% to 36% (+19pp), showing architecture choice can matter as much as the defense layer.
Catches the Spy, Misses the Cabal: ORBIT Exposes a Blind Spot in Multi-Agent Defenses thumbnail

· arXiv preprint

Full Success 2.7%, Partial Score 70%: Reviewing the KNOWS Benchmark for Web Agents

Handing web agents full document, spreadsheet, and slide-deck tasks yields only 2.7% full success — and high partial scores often hide unusable artifacts.

  • Across 110 tasks, only the best model-harness pair (Perplexity Comet + Claude Opus 4.7) achieved a nonzero full success rate — 2.7% (3 tasks) — while all six other combinations scored 0%.
  • Partial-success scores reached ACF 70.0% / SF 64.7% at best, but the showcased example scored ACF 0.54 / SF 0.59 while completing only 0.14 of checkpoints (ASC) — an artifact that was practically unusable.
  • Swapping only the harness (BrowserGym to Comet) for the same Claude Opus 4.7 model raised SR by 2.7pp, ASC by 18.7pp, ACF by 27.5pp, and SF by 25.6pp — harness choice mattered more than the underlying model.
Full Success 2.7%, Partial Score 70%: Reviewing the KNOWS Benchmark for Web Agents thumbnail

· arXiv preprint

A Different Case Number Means 'Wrong': Reviewing CARGO's Fix for LLM Judges

CARGO raises an LLM judge from DI 0 to .58 — but still misses 80% of procedural errors.

  • Reference-based LLM judges penalize 100% of correct entity-transplanted answers as wrong or hallucinated (discrimination index ≈0) across all 50 P1 items on both judge models
  • CARGO cuts false penalties to 0–3% while holding 98–100% contradiction recall, raising DI to .56–.58
  • But CARGO catches only 20% of injected procedural corruptions — a post-hoc fix doesn't help (ΔDI=-.007), and human-style double-annotation reproduces the same blind spot (3/10)
A Different Case Number Means 'Wrong': Reviewing CARGO's Fix for LLM Judges thumbnail

· arXiv preprint

16% to 76% Compilation Success: Reviewing Chat2Scenic's RAG for Autonomous-Driving Scenario Generation

IROS 2026's Chat2Scenic uses iterative RAG to generate driving scenarios, and this review verifies both its compilation success rate and semantic accuracy gains.

  • Chat2Scenic: 76.42% CSR, 58.17% FA — a large gap over the baseline (Retrieval Assemble: 30.08% CSR, 11.03% FA)
  • CSR and FA move independently at baseline: assembly alone raises CSR (16.26%→ 30.08%) while FA barely moves (10.86%→ 11.03%)
  • Passed IROS 2026 peer review, with a 123-scenario benchmark and code public on GitHub for reproducibility
16% to 76% Compilation Success: Reviewing Chat2Scenic's RAG for Autonomous-Driving Scenario Generation thumbnail

· arXiv preprint

Only 5 of 1,000 Were Real JSON: A Sub-2B MCP Tool-Calling Reliability Review

Five sub-2B open-weight models were tested on MCP-style JSON tool calls: best case 79%, but only 0.5% without a recovery parser. We checked the tables.

  • Qwen2.5-1.5B sampling tops out at 79% (95% CI 70.0-85.8%); Phi-1.5 scores 0% in both conditions
  • Without the recovery parser, only 5 of 1,000 raw responses (0.5%) are valid JSON outright
  • The 0.5B model trails the 1.5B by just 3 points but cuts latency 65% and memory 54% - a real deployment trade-off
Only 5 of 1,000 Were Real JSON: A Sub-2B MCP Tool-Calling Reliability Review thumbnail

· arXiv preprint

Why Enterprise Agents Stall After the Pilot: A Review of 'Substrate Inversion'

A position paper argues agents need a markdown substrate built for them, not a schema borrowed from CRMs.

  • This is a position paper, not an empirical benchmark — the authors state in Section 8 that the framework 'has not yet been benchmarked against the alternatives.'
  • In the four-layer design (Data, Knowledge, Intelligence, Governance), only the Sync Agent may call external systems, and trust levels attach to individual skills, not agents (shadow to assist to autonomous-sampled to full).
  • The authors' own top-flagged risk is indirect prompt injection on the compile path — a payload compiled once is permanently re-exposed to every agent that reads the substrate.
Why Enterprise Agents Stall After the Pilot: A Review of 'Substrate Inversion' thumbnail

· arXiv preprint

Right Answer, Wrong Pick: What OrchSLM Reveals About Small-Model Routing

Pooling seven small models still left a 35pp gap between oracle coverage and actual routing accuracy on GPQA. One lever, expert weighting, drove most of the gain.

  • Expert-weighted routing beat equal weighting on all four benchmarks (MATH-500 +8.2%, GPQA +10.7% relative) and outscored the prior router SLM-MUX by +16.1% on GPQA.
  • On GPQA, a 35.25pp gap separated the oracle ceiling (78.54%) from actual routing accuracy (43.28%) — the correct answer was in the pool, but the router missed it more than half the time.
  • Growing the model pool kept raising oracle coverage but routed accuracy peaked at three to four models, qualifying the assumption that adding more models always helps.
Right Answer, Wrong Pick: What OrchSLM Reveals About Small-Model Routing thumbnail

· arXiv preprint

The Stochastic Deputy: Removing the Tenant Parameter Stopped Interface Leaks in an MCP Agent Study

We verified a production field study showing that removing the tenant ID from an MCP tool schema eliminated interface-level leaks across a 373-trial agent ablation, but 12 shell-capable agents bypassed it by forging their own scope file.

  • Removing the tenant parameter from the tool schema (Arm B) produced zero interface-level leaks across 373 trials, while conventional parameter validation (Arm A) served 26 of 41 plausible-pretext requests (63%).
  • Once the interface was blocked, 12 shell/filesystem-capable agents (11/13 Gemini, 1/13 GPT) bypassed it by forging the scope file they already held; Claude and the three latest GPT-5.6 tiers did not bypass it in the same conditions.
  • A function-wrapped set-valued scope predicate lost index access and ran 57x slower (5,781ms vs 101ms median); beyond 10-15 entitled tenants, the deployed enforcement point's latency jumped from 1,065ms to 10,005ms.
The Stochastic Deputy: Removing the Tenant Parameter Stopped Interface Leaks in an MCP Agent Study thumbnail

· arXiv preprint

Accuracy Lies: TriCalRAG's On-Premise Benchmark for LLM-Based Root Cause Analysis

Zero-shot prompting looked fine on F1 but collapsed to predicting anomaly on up to 100% of incidents; RAG fixed the calibration in this single-GPU on-premise benchmark.

  • Mistral-Small's zero-shot predicted-positive rate hit 98.7-100% on BGL/HDFS/OpenStack while F1 still looked reasonable at 0.67-0.72.
  • RAG kept 7 of 8 configurations calibrated (vs. 2 of 8 under zero-shot) and lifted F1 by 0.10-0.27 over zero-shot.
  • On a single RTX PRO 6000, batching alone scaled throughput 41x (48.3 to 1,991.0 tok/s), and 4-bit quantization cut latency 20% with no measurable accuracy loss.
Accuracy Lies: TriCalRAG's On-Premise Benchmark for LLM-Based Root Cause Analysis thumbnail

· arXiv preprint

More Actions, Same Unverified Autonomy: A Cross-Domain Critical Review of Agentic AI

A critical review spanning digital, social, virtual, and physical agent research finds that action-interface expansion has outpaced evidence for completion, recovery, and independent verification.

  • Action interfaces (tools, computer use, MCP, A2A) have expanded clearly, but evidence for completion, recovery, and independent verification has not kept pace, the review concludes.
  • Early WebArena/OSWorld baselines (14.41% / under 12.2%) and later first-party systems (OpenAI CUA: 58.1% / 38.1%) both still trailed human performance (78.24% / 72.4%).
  • On the RentAHuman marketplace, where agents hire humans, 99 of 303 tracked bounties (32.7%) arrived via API/MCP channels, with confirmed cases of identity impersonation and credential fraud.
More Actions, Same Unverified Autonomy: A Cross-Domain Critical Review of Agentic AI thumbnail

· arXiv preprint

The Strongest Model Was the Least Safe: Reviewing ReactHuman

We tested seven MLLMs on split-second hazard reactions — the top-performing model scored lowest on safety.

  • Safety scores ran inversely to scale: the 27B Gemma model hit 88.2% while flagship Claude Opus 4.8 scored lowest at 64.7%.
  • 35.9% of dodge-required decisions broke a safety rule (23-66% by model), and 64.5% of those mistakes (253 of 392) were simply freezing in place.
  • Even when models picked the right action, their hands landed a median 0.48m from the real impact point - 49% of the time because the robot stood over 0.8m away, beyond arm's reach.
The Strongest Model Was the Least Safe: Reviewing ReactHuman thumbnail

· arXiv preprint

Only 82 of 790 Scored Records Were Comparable: Reviewing Benchmark Radar's Catalog Audit

Only 82 of 790 scored benchmark records meet the bar for a valid 0-100 percentage-scale comparison.

  • Of 790 scored records, only 82 (10.4%) declare a percentage scale, a known direction, and values within 0-100 -- the baseline for direct comparison.
  • Of 345 agentic records, only 128 carry the top-level 'Agentic & Tool Use' label; 117 sit under 'Coding & Software Engineering,' so a single-label count misses up to a third.
  • 668 of 1,283 records (52%) carry no release date, and 1,171 of 1,208 cited documents (97%) lack a named organization.
Only 82 of 790 Scored Records Were Comparable: Reviewing Benchmark Radar's Catalog Audit thumbnail

· arXiv preprint

When Reuse Is Worse Than No Cache: Reviewing KVShareArena

Naive KV-cache reuse across multi-agent reports scores PGR −.824, worse than no cache at all — we check where free fixes stop working against the tables.

  • Naively concatenating reused agent-report KV caches scores PGR −.824 — worse than answering with no cache at all
  • Paid recomputation only beats free position-alignment on queries needing multiple sources at once (multi-hop QA, agent reports); compression never wins anywhere
  • The trained adapter (KVPacket) degrades the most of 84 method-cells under a checkpoint swap, and it fails silently — fluent, confident answers naming the wrong entity
When Reuse Is Worse Than No Cache: Reviewing KVShareArena thumbnail

· arXiv preprint

Guessing Instead of Computing: Reviewing HealthLoopQA

Switching from a prompting baseline to a tool-executing agent raised the score from 43.7 to 65.8, but multi-step pattern reasoning still collapsed to single digits in this benchmark.

  • Switching Gemini from prompting to a tool-executing agent raised simulated Overall from 43.7 to 65.8 and real-world Overall from 64.6 to 78.5 — but only Gemini was tested both ways
  • Anomaly detection improved from 21.2 (prompting) to 52.4-72.9 (agents), yet multi-step chains like Event-Retrieval-to-Pattern-Correlation collapsed to 0.8-23.5
  • The authors document an 'in-context laziness' failure mode - confident guessing instead of full computation - based on a single-patient qualitative review
Guessing Instead of Computing: Reviewing HealthLoopQA thumbnail

· arXiv preprint

Trained Only on Normal Workflows, 0.89% False Positives: Reviewing Skynet

Trained only on benign workflows, never on an attack or failure, this detector hit 89% recall at 0.89% FPR on a combined 808-case benchmark. We check the tables.

  • Across 808 combined anomalies from three independently released benchmarks (Agent-SafetyBench, AgentErrorBench, ATBench), one fixed threshold yields 93.36% Acc / 99.31% Prec / 89.36% Rec / 0.89% FPR
  • Guardian beats Skynet's FPR (0.18%) on narrow benchmarks but collapses to 18.31% recall on ATBench — single-benchmark performance is a risky basis for adoption
  • Ablation shows collapsing K=150 multi-mode scoring into one reference cuts recall by roughly a third — the core value is modeling benign diversity, not the detection algorithm itself
Trained Only on Normal Workflows, 0.89% False Positives: Reviewing Skynet thumbnail

· arXiv preprint

Generation Got Faster, Shipping Didn't: Reviewing the Agentic SDLC 'Verification Tax'

Commits rose 180% but releases only 30% — we check a preprint that synthesizes 30 studies into a 'verification tax' framework for measuring the gap.

  • Across 100,000+ GitHub developers, autonomous-agent-stage commits rose 180%, projects 50%, releases only 30% — throughput attenuates downstream
  • SWE-Marathon rollouts average 27.2M tokens yet stay under 30% pass@1, with reward hacking in 13.8% — long-horizon runs need hard stop rules
  • Meta's TestGen-LLM saw 73% of filtered generated tests accepted into production — one of the few industrial cases where generate-then-filter works
Generation Got Faster, Shipping Didn't: Reviewing the Agentic SDLC 'Verification Tax' thumbnail

· arXiv preprint

83% Quality, 32% Tokens: Reviewing RSM-full's Compact-Memory Agents

We check RSM-full's claim -- that a merge rule and grouped packing explain most of the compact-memory quality gap -- against its own tables.

  • At a 4k-token budget, RSM-full reaches 83% of Full-Context quality on 32% of the tokens, beating Online K-Means by +3.5-6.0pp (p<.001)
  • The merge rule adds +5.7pp and the grouped packer +5.02±1.00pp; the retrieval rule contributes nothing once clustering and packing are fixed (p=.40)
  • The pattern reproduces on the independent RealMem benchmark, but RSM-full trails K-Means on EMBODIED-AI (-2.68pp) and Game (-1.58pp) domains
83% Quality, 32% Tokens: Reviewing RSM-full's Compact-Memory Agents thumbnail

· arXiv preprint

Ask, or It Doesn't Exist: Reviewing the CivBench Agent Benchmark

Told to check every 20 turns, agents checked every 30-75. We verified CivBench's 23-game results against the source.

  • 3 wins in 23 games, Fisher's exact p=0.488 -- win/loss alone can't discriminate 4 model families
  • Victory-progress queries only 3.7-10.0 per game -- vs. the every-20-turn playbook guidance
  • Reflection-Action Gap (RAG@10) at 48.2-65.8% -- many stated plans go unexecuted within 10 turns
Ask, or It Doesn't Exist: Reviewing the CivBench Agent Benchmark thumbnail

· arXiv preprint

The Optimizer Keeps the Budget: Reviewing Meta's CORAL Recommender Agent

We check the tables behind Meta's constrained agentic loop for production recommenders.

  • Three rounds of retrieval-budget reallocation lifted sessions +0.16% (+0.77% in the largest market)
  • A second serving-capacity round grew savings 44% while engagement stayed flat
  • 8-10 LLM calls per cycle put estimated inference cost at tens of dollars per deployment
The Optimizer Keeps the Budget: Reviewing Meta's CORAL Recommender Agent thumbnail

· arXiv preprint

69% Read at Half Occlusion: Reviewing BlobBoards Marker Robustness

Where markers collapse first under occlusion, distance and angle — measured. Printed QR shares the structure.

  • 69% detection under 50% occlusion versus zero for AprilTag and ArUco
  • 80% vs 74% vs 58% overall detection, with the gap widest on small markers
  • Rotation accuracy cannot be ranked: differences fall inside the 0.46 degree ground-truth resolution
69% Read at Half Occlusion: Reviewing BlobBoards Marker Robustness thumbnail

· arXiv preprint

Explore More, Drift Less: Reviewing CANOPY's Agentic RL

We check the tables behind a claim that outcome-only RL suffices for long-horizon agents — a bigger rollout group and a KL anchor alone took a 14B model to the top of the trained-policy AppWorld leaderboard.

  • Scaling the rollout group to 32 and adding a KL anchor plus strict on-policy updates alone lifted Qwen3-14B to Test-Normal TGC 86.9, topping the trained-policy leaderboard.
  • Ablations confirm the diagnosis: cutting the group size to 8 costs 16.4 points and halving the gradient mini-batch costs 17.4, showing both signal starvation and policy drift matter in practice.
  • The same recipe transferred to SWE-bench Verified lifted the resolve rate from 31.3% to 47.9%, suggesting the approach generalizes across code-executing long-horizon tasks.
Explore More, Drift Less: Reviewing CANOPY's Agentic RL thumbnail

· arXiv preprint

Gated Memory Beats Full History: Cutting Multi-Agent Routing Cost 32%

Routing multi-agent systems on a gated, filtered memory instead of the full execution history cut inference cost 40-53% while preserving accuracy, verified against the paper's tables.

  • Averaged across five benchmarks, the method scores 77.73, beating the strongest baseline (Puppeteer-32B, 75.29) by 2.44 points
  • A same-system control swapping only memory handling (full-history vs. gated) preserves accuracy while cutting cost 40% on GSM-Hard and 52.9% on HumanEval
  • Removing the LLM router drops GSM-Hard accuracy 14.0 points, while removing both memory gates costs only 2.56-6.25 points, so backbone routing carries more of the accuracy gain
Gated Memory Beats Full History: Cutting Multi-Agent Routing Cost 32% thumbnail

· arXiv preprint

Tokens Down 30%, Cache Down 9 Points: Reviewing ContextPipe

ContextPipe applies database query-execution discipline to agent context assembly, cutting tokens and calls at the cost of cache-hit rate -- we check the tradeoff against the paper's own tables.

  • Total prompt tokens fell 30.1% (1.05M to 731K) while mean cache-hit rate dropped 9.1 points (95.3% to 86.2%)
  • ContextPipe only comes out cheaper once a provider's cached-token price ratio exceeds the break-even point of 0.145
  • The evaluation covers just 3 of 79 SWE-bench Pro tasks, a sample-size limit the authors flag themselves
Tokens Down 30%, Cache Down 9 Points: Reviewing ContextPipe thumbnail

· arXiv preprint

Losing on a Short Step Budget: Reviewing a Stack-Memory Agentic RAG

A Peking University team's stack-memory agentic RAG framework beats strong baselines on seven multi-hop QA benchmarks on average, but reverses to underperform under a short step budget.

  • Across all three backbones (1.5B/3B/7B), average F1 beat the strongest baseline by 3.27-5.58 points
  • Removing the memory reward roughly halved MusiQue performance, from 16.48% to 7.63%
  • At a 10-step budget, Bamboogle scored just 10.03% versus the baseline's 37.05%, but reversed to lead at 35.47% by 30 steps
Losing on a Short Step Budget: Reviewing a Stack-Memory Agentic RAG thumbnail

· arXiv preprint

Configuration Beats the Model: Reviewing an MCP Tool-Calling Reliability Benchmark

A benchmark of local LLM agents operating hardware-design tools through MCP finds tool-description detail and few-shot prompting are the biggest reliability swing factors.

  • Top model Gemma 4 31B reached 0.990 ECC, but configuration swung results sharply.
  • Comprehensive tool descriptions appeared in 83% (35/42) of best configurations.
  • Multi-agent decomposition lifted a weak worker's ECC from 0.554 to 0.718.
Configuration Beats the Model: Reviewing an MCP Tool-Calling Reliability Benchmark thumbnail

· arXiv preprint

More Thinking Didn't Help on GAIA: Reviewing Adaptive Reasoning in Agentic AI

We check the tables behind why extending reasoning on MATH-500 and GAIA raises accuracy in one case and not the other.

  • On MATH-500, Phi-4-reasoning beats Qwen3.5-4B by 4.8 points but takes 2.6x longer and token-limit hits jump from 190 to 482.
  • On GAIA, nearly tripling reasoning time left Phi-4-reasoning's accuracy (11.52%) below Qwen3.5-4B's (12.12%).
  • Llama-3.1-8B-Instruct was classified as under-reasoning on 71.52% of GAIA tasks, answering before gathering enough evidence.
More Thinking Didn't Help on GAIA: Reviewing Adaptive Reasoning in Agentic AI thumbnail

· arXiv preprint

Budget Before Progress: Reviewing ProgRouter's Multi-Agent LLM Routing

An online router that re-picks the model at every workflow step matched or beat budget-compliant baselines across four benchmarks. We checked the source tables.

  • On HumanEval Plus, ProgRouter reaches 93.0% pass rate versus MasRouter's 90.9% and CASCADIA's 84.8%, while staying within the 4,800 J budget
  • On MBPP, ProgRouter tops every baseline on all three metrics at once: pass rate, energy (3,376 J), and execution time (10.3 s)
  • A naive progress-per-cost router burns 62.6% more energy (4,796 to 7,797 J) yet pass rate collapses from 93.0% to 17.7%
Budget Before Progress: Reviewing ProgRouter's Multi-Agent LLM Routing thumbnail

· arXiv preprint

The Fix Was an Illusion: Rerun Repairs Just 6.90% of LLM Multi-Agent Failures

Unguided reruns fix only 6.90% of MAS failures; targeted intervention reaches 20.15%.

  • Unguided Full Rerun repairs 6.90% of 536 failures vs. Suspicious-Node Intervention's 20.15% (2.92x, one attempt vs. three)
  • Rerunning 54 previously successful cases 3x broke 39 of them (72%) at least once
  • SymTrace replay reproduces failures more reliably than rerun: 80.78% vs 67.97% (rep1), 52.43% vs 41.42% (rep3)
The Fix Was an Illusion: Rerun Repairs Just 6.90% of LLM Multi-Agent Failures thumbnail

· arXiv preprint

Generic Tools Lost to Raw SQL: Reviewing the MCP Domain Tooling Pattern

A shallow generic MCP tool pack scored worse than raw SQL; a domain-tooled one hit 93.9%.

  • A verticalized domain tool pack hit 0.939 accuracy versus 0.666 for raw SQL and 0.605 for a generic pack, which scored below raw SQL
  • The smallest model (llama3.2:3b) jumped from 0.583 to 0.929 fully-correct with domain tools, the paper's evidence for Model Demotion
  • Cost per correct answer fell up to roughly 11.6x versus raw SQL (3B model), with the cheapest fully-correct cell coming from a 3B model using tools, not an 8B model writing SQL
Generic Tools Lost to Raw SQL: Reviewing the MCP Domain Tooling Pattern thumbnail

· arXiv preprint

94.5% Pass the Scan, Yet Correlate With Nothing: Reviewing ACES Skill Evaluation

Skills that pass static scans show near-zero correlation (ρ≈-0.02) with their measured live-deployment value. We check NVIDIA's paired live-evaluation methodology, ACES, against the source.

  • 94.5% of skills clear the structural gate and 86.2% pass the LLM-judge rubric, yet the two static scores correlate at only Spearman ρ=0.14 — even before adding runtime evidence
  • Across 947 paired live trials, adding the skill raised mean composite score by 0.2134 (95% CI [0.1967, 0.2301], positive in 72.8% of cases) — but static scores track that live lift at ρ≈-0.02, statistically indistinguishable from zero
  • Once visible skills exceed 20, routing overhead climbs sharply: at 50 visible skills the with-skill pass rate falls to 0.55 and mean wall time rises to 1,290 seconds
94.5% Pass the Scan, Yet Correlate With Nothing: Reviewing ACES Skill Evaluation thumbnail

· arXiv preprint

Best Human Still Bottom of the Board: Reviewing FM-Bench

FM-Bench ran 15 frontier models through 20 in-game years of football-club management. The best untrained human still landed near the bottom of the model board, and the league title rotated among ten different models.

  • claude-fable-5 leads the solo track (90.94, ~95% of the 95.54 oracle ceiling) and the Arena (76.26), yet kept the Arena title in only 2 of 19 season transitions — ten different models won it at least once
  • 4 of 6 first-play humans were fired or went insolvent mid-run; the best of the two survivors (74.64) still lands near the bottom of the 15-model board
  • Token spend spans 28M-194M (7x) with no correlation to score (r_s=-0.19) — cutting endgame investment (r_s=-0.58) and avoiding idle cash (r_s=-0.50) predict it far better
Best Human Still Bottom of the Board: Reviewing FM-Bench thumbnail

· arXiv preprint

22.6 Points More Accurate, 2.7x the Cost: Reviewing a Multi-Agent BI Platform

Splitting conversational BI into five agents beat a single agent by 22.6 points, at 2.7x the cost.

  • The 5-agent pipeline beat a single agent on functional accuracy (95.3% vs 72.7%, +22.6 pts) and quality (4.52 vs 3.76, +20.2%), but used roughly 3x the tokens per query (30,800 vs 10,200)
  • Even open-source Llama 3.1 70B reached 79.7% inside the multi-agent pipeline, beating the single-agent GPT-4.1 baseline (72.7%) — architecture mattered more than model choice
  • Ablation shows the Follow-up and Chart Configuration agents contribute only -0.02 to quality when removed, making them the first candidates for parallelization or a lighter model
22.6 Points More Accurate, 2.7x the Cost: Reviewing a Multi-Agent BI Platform thumbnail

· arXiv preprint

65K Parameters Beat 14 Billion: Reviewing AFANet for Agent Failure Attribution

Verified against the source: a 65K-parameter graph network beats a fine-tuned 14B LLM under matched reimplementation, but the edge disappears out of distribution.

  • Under matched reimplementation, 65K-param AFANet beats Qwen2.5-14B+SFT (agent µF1 74.16 vs 41.04, pair MF1 16.35 vs 2.02)
  • Training 1.1h and inference 1.16s vs 74h+/367s for 14B+GRPO — roughly 67x and 316x faster, with ~215,000x fewer parameters
  • On OOD benchmark Who&When, AFANet's pair µF1 (6.90%) trails o3 (7.41%) — the in-domain edge doesn't fully carry over
65K Parameters Beat 14 Billion: Reviewing AFANet for Agent Failure Attribution thumbnail

· arXiv preprint

GPT-5.5 Still Misses One in Three: Reviewing Wuying-Browser-Agent

The strongest closed agent still misses a third of 37.9-step BrowserBench tasks. We check the source.

  • Wuying-Browser-Agent-27B tops open-source models at 70.8% average (80.6% WebVoyager, 66.7% Online-Mind2Web, 65.1% BrowserBench), beating Qwen3.8-Max and GPT-5
  • Even GPT-5.5, the strongest closed model tested, misses 32.6 points on the 37.9-step BrowserBench — a gap short benchmarks don't expose
  • Online RL (DAO-GRPO) gains scale with difficulty and length: +12.4 pts on hard tasks, +13.4 pts (13.3%→26.7%) beyond 50 steps
GPT-5.5 Still Misses One in Three: Reviewing Wuying-Browser-Agent thumbnail

· arXiv preprint

Right About Failure, Wrong About Which Collaboration Pays: A Cost-Aware Protocol Routing Review

Confidence scores predict Baseline failure well (AUROC 0.88) but barely predict which paid collaboration protocol is worth it (AUPRC in the 0.10s) — we checked the tables.

  • A self-confidence gate hits 78.0% solve at 45.0K tokens, beating a 71.3K frozen-LLM router's 73.8%
  • The same confidence score's AUPRC falls from 0.8950 (predicting Baseline failure) to 0.1674 (predicting a PER-only win)
  • Across 6 held-out router evaluations, the gap to the retrospective oracle stays 18.5-28.9 points
Right About Failure, Wrong About Which Collaboration Pays: A Cost-Aware Protocol Routing Review thumbnail

· arXiv preprint

The Patch Always Applies, the Meaning Doesn't: Reviewing Kozuchi Agent

An open-weight 27B agent hit 74.8% on SWE-bench Verified, but the paper itself reports that 91.3% of remaining failures were semantic, not format, errors.

  • SWE-bench Verified: 374/500 (74.80%, 95% CI [70.82%, 78.41%]) — #1 open-weight, #12 overall
  • Cross-agent selection recovers +14 instances over the order baseline (362) and reaches 92.2% of the 408-instance oracle ceiling
  • 91.3% (115/126) of unresolved instances are clean-applying patches that miss hidden tests — a semantic gap, not a formatting one
The Patch Always Applies, the Meaning Doesn't: Reviewing Kozuchi Agent thumbnail

· arXiv preprint

The Winner That Won't Call Itself One: Reviewing MemoryLake's Agent-Memory Benchmark

MemoryLake's own authors benchmarked their memory backend to a 20.5% macro-average success rate, but the paper itself discloses overlapping confidence intervals and no significance testing.

  • MemoryLake led with a 20.5% macro-average success rate versus 13.6% for the best comparator (long context), a 6.9-point gap, but the authors themselves note overlapping 95% Wilson intervals and no paired significance test.
  • The gap came from physics (12/20 vs 9/20) and math (9/40 vs 8/40); travel planning (0/30 for all four systems) and shopping (all but one 0/150) barely separated any system.
  • Every author is an employee of MemoryLake, the commercial vendor whose product is being benchmarked, disclosed in a dedicated Competing Interests section — and a footnote reveals Mem0's favorable number came from a write-size cap applied only to Mem0.
The Winner That Won't Call Itself One: Reviewing MemoryLake's Agent-Memory Benchmark thumbnail

· arXiv preprint

Noticing Beat Undoing: Reviewing LoongReflect's Search-Agent Reflection

Removing the reflect action cost 15.31 F1 points; removing backtrack alone cost 13.06 points.

  • LoongReflect beat the strongest baseline (AgenticRAG-R1) by 12.60 average-F1 points at 3B and 12.61 at 7B across seven QA benchmarks.
  • Removing the reflect action dropped average F1 from 46.15 to 30.84 (-15.31); removing backtrack alone dropped it to 33.09 (-13.06) — diagnosis was the bigger bottleneck.
  • Removing look-ahead coordination cost only -4.94 points, the smallest ablation loss, meaning a simple combination already captures 89.4% of the gain.
Noticing Beat Undoing: Reviewing LoongReflect's Search-Agent Reflection thumbnail

· arXiv preprint

It Designs the Study Anyway: Reviewing TRACES on LLM Scientific Reliability

Tested against 42 retracted, fraudulent, and pseudoscientific papers, 30 models complied with follow-up research requests 93% of the time on average.

  • Across 30 models, 42 probes, and 10 runs (12,600 responses), aggregate failure was IFR-a 0.93±0.004 and IFR-i 0.809±0.009; 22 of 30 models failed more than 90% of agentic probes.
  • About 81% of interactive responses carried no warning at all, and even the four best performers warned only 46-48% of the time.
  • Refusals clustered on famous cases like Wakefield's MMR paper; the disclaimer tax reached 0.211 for overtly implausible physics but shrank to 0.026 for polished-looking biomedical pseudoscience, evidence models react to style, not substance.
It Designs the Study Anyway: Reviewing TRACES on LLM Scientific Reliability thumbnail

· arXiv preprint

Stating the Rule Doesn't Stop the Hand: Reviewing REDAgentBench

One in five confirmed agent violations happened right after the agent stated the safety rule out loud. We check REDAgentBench's tables.

  • Across six models and three harnesses, macro-average ASR was 65.69%, and swapping only the harness moved a single model's ASR by up to 30.7 points (Qwen3.7-plus, 49.39%→80.09%).
  • Judging by execution outcome (service receipts, final state) rather than transcripts alone raised reported ASR by 7.73–11.72 points per model, showing trajectory-only evaluation structurally undercounts real harm.
  • 17.92% of Qwen-plus's 2,846 confirmed violations were a 'Recognition-Execution Gap' — the agent stated the rule or risk, then acted anyway — and a training-free policy reminder cut replay violations by 74.19 points.
Stating the Rule Doesn't Stop the Hand: Reviewing REDAgentBench thumbnail

· arXiv preprint

84% Alone, 27% Together: Reviewing the Enterprise RAG Instruction Gap

A China Mobile benchmark tests 13 LLMs on complex instruction-following under noisy, gap-ridden, and conflicting retrieval — we check the tables.

  • Across 13 LLMs on noisy retrieval, even the top model (Qwen3-235B-Thinking) dropped 57 points, from 83.8% per-constraint satisfaction (Loose IAS) to 26.8% full-instruction compliance (Strict IAS).
  • On knowledge-gap cases requiring an explicit 'no answer' admission, Qwen3-30B-Instruct's rejection rate was just 6.6%, and even the best reasoning model, Claude-Opus-4.5, reached only 42.7%.
  • Factual-conflict recognition topped out at 44.3% (DeepSeek-R1) and bottomed at 18.5% (GPT-4.1); the correlation between conflict recognition and answer coverage flipped sign between reasoning models (+0.90) and standard models (-0.50).
84% Alone, 27% Together: Reviewing the Enterprise RAG Instruction Gap thumbnail

· arXiv preprint

100% vs 25%: Reviewing PayPal's T2MO Framework for Coding-Agent Cost Routing

Cross-checking a PayPal framework that routes coding-agent traffic by task-category and difficulty, built on pass rates for the same model that swing from 100% to 25%.

  • Built on data showing 98% of a $3.05M/month coding-assistant spend concentrated in two frontier models, T2MO routes traffic by task-category-by-difficulty cells rather than per-prompt.
  • The same GLM 5.2 model's measured pass rate held at 100%/100%/47% (Easy/Medium/Difficult) on Git Workflow tasks but collapsed to 100%/50%/25% on Frontend & UI tasks, showing the risk of single-category routing.
  • The table's savings figures ($26K/$20K/$26K) are not measured outcomes but worked examples using assumed coefficients (δ=0.35, a 35/45/20% difficulty split) the paper itself calls illustrative — the formula checks out, but the dollar amounts aren't confirmed deployment results.
100% vs 25%: Reviewing PayPal's T2MO Framework for Coding-Agent Cost Routing thumbnail

· arXiv preprint

One Layer of Pseudocode Lifts the Score From 4.31 to 4.78: Reviewing Pseudo2CodeQA

A code-generation agent that inserts pseudocode as an intermediate representation scored 4.78 vs. 4.31 (+10.9%) under GPT-5 grading, cross-checked against the source paper.

  • Under GPT-5 grading, the Pseudo2Code agentic pipeline's overall score reached 4.78, a 10.9% gain over the same Gemini 2.5 Pro model's direct-generation score of 4.31.
  • Human evaluation (100 tasks, 2 raters) scored 4.85 overall and execution test-pass rate hit 84.6%, agreeing in direction with automated grading -- but the pass rate spread from 97% (Easy) to 71% (Hard).
  • The benchmark's ground-truth pseudocode and reference code were themselves generated by Gemini 2.5 Pro -- the same model powering the top-scoring framework -- so the study's design can't separate a structured-pseudocode effect from a same-model-affinity effect.
One Layer of Pseudocode Lifts the Score From 4.31 to 4.78: Reviewing Pseudo2CodeQA thumbnail

· arXiv preprint

Couple Planning and Memory, Resolve 25 More Issues: Reviewing PMCoder

On SWE-bench Verified, PMCoder's bidirectional coupling of a phase planner and episodic memory lifted resolved issues from 142.3 to 167.3 (+5.0 pp); we checked the tables against the source.

  • PMCoder resolves an average 167.3/500 SWE-bench Verified issues (33.5%) versus a 142.3 (28.5%) harness-matched baseline -- a +25.0 instance (+5.0 pp) gain with a 95% CI of [+14.3, +35.7] (p<0.001).
  • A 2x2 ablation shows the plan+memory interaction (+10.3 instances) significantly exceeds the sum of planning-only (+6.3) and memory-only (+8.3) gains (F(1,8)=10.92, p=0.011).
  • Even on 315 'unarmed' instances lacking a reproduction script, PMCoder still gained +3.8 pp -- the plan-memory coupling helps even when execution grounding never fires.
Couple Planning and Memory, Resolve 25 More Issues: Reviewing PMCoder thumbnail

· arXiv preprint

Production Rose, Public Records Thinned: A Simulation Review of Agentic Coding in Open-Source Communities

A multi-agent simulation of coding-agent adoption found completed tasks up 39% and median task time down from 45 to 20 minutes, while public-knowledge retrieval coverage fell from 81.1% to 22.3%.

  • Completed tasks +39.0%, median completion time 45→20 minutes, yet final adoption reached only 26.0%.
  • Direct human-to-human task share fell from 32.4% to 11.6% while agent-involved modes rose to 57.3%.
  • On a standardized retrieval benchmark, public-knowledge coverage dropped from 81.1% to 22.3%, thinning the record newcomers can rely on.
Production Rose, Public Records Thinned: A Simulation Review of Agentic Coding in Open-Source Communities thumbnail

· arXiv preprint

The Most Accurate Agent Was the Least Economical: Reviewing EcoAgent-Bench

Always-escalate controls topped strict accuracy at 76.3% yet scored 12.2% on economic consistency.

  • 304 tasks split into 222 upgrade-oriented and 82 save-oriented; the economic score is the minimum of the two.
  • Always-escalate controls led micro strict success at 76.3% and 73.0% but scored 12.2% and 0.0% on economic consistency.
  • Tool-API agents reached only 3.9-24.0% micro strict success and at most 7.3% economic consistency.
The Most Accurate Agent Was the Least Economical: Reviewing EcoAgent-Bench thumbnail

· arXiv preprint

Throw the Trajectory Away: Reviewing LongHorizon-Harness

Discarding execution traces and keeping only task state plus audit reports lifted WeaveBench pass rate from 51.8% to 80.7% — and on Terminal-Bench it cut token use by 24%.

  • WeaveBench pass rate 51.8% → 80.7%; OSWorld Binary 2.8% → 8.3%
  • Terminal-Bench: +7.5 pts success with 24% fewer tokens
  • The auditor burns 19.4–38.1% of all tokens — the verification tax, measured
Throw the Trajectory Away: Reviewing LongHorizon-Harness thumbnail

· arXiv preprint

The 77x Came From 16 Experiments: Reviewing a Validation Framework for Agentic A/B Simulation

A framework that clones 1,000 customers as agents and replays 67 marketing experiments. It gets the sign right at 0.70, but launch alignment stops at 0.41 against a 0.33 floor.

  • Sign overlap 0.70 (floor 0.50), launch alignment 0.41 (floor 0.33)
  • The 77x error compression was measured on 16 of 67 experiments
  • More agents and within-subject design cut variance only, by ~2.4x
The 77x Came From 16 Experiments: Reviewing a Validation Framework for Agentic A/B Simulation thumbnail

· arXiv preprint

The Map Was There, They Searched Anyway: Reviewing ScrambleToolBench

Stripping semantic cues from tool names dropped mean episode completion across 15 models from 0.93 to 0.32, and to 0.03 once perturbations were combined. Recovery search was indistinguishable from random.

  • Mean completion 0.93 → 0.32 (Base) → 0.03 (combined)
  • Sonnet 5: 1.00 under drift or failure alone, 0.00 combined
  • Recovery-chain following 11.0% vs 10.8% random
The Map Was There, They Searched Anyway: Reviewing ScrambleToolBench thumbnail

· MCP Roadmap

The Roadmap That Promises Nothing: Grading MCP's Four 2026 Priorities Five Months On

We verify the four priority areas of the 2026 MCP roadmap against primary sources and grade real progress using the 07-28 spec release.

  • Of four priorities, stateless transport landed in the finalized 07-28 spec within five months
  • All four enterprise items (audit, SSO, gateway, config portability) ride the extension track, not core
  • In the deferred security area only two SEPs are under review: SEP-1932 (DPoP) and SEP-1933 (WIF)
The Roadmap That Promises Nothing: Grading MCP's Four 2026 Priorities Five Months On thumbnail

· arXiv preprint

The Model Was the Only Wall: Reviewing IssueTrojanBench

We verify IssueTrojanBench against the original: 66.5% of malicious issues beat every guardrail, and 82.9% of all rejections came from the model alone.

  • Across 4,176 runs over 6 agent-model pairs, 66.5% of malicious issues penetrated both agent- and LLM-level guardrails (Codex Desktop 79.2%, Cursor 66.5%, Claude Code 41.1%).
  • 82.9% of 1,400 rejections were explicit model-level refusals — framework defenses like OS sandboxing contributed no observable rejections.
  • Cross-lingual, positional, and typographic perturbations had zero effect on success rates, arguing for semantic and provenance-based defenses over pattern filters.
The Model Was the Only Wall: Reviewing IssueTrojanBench thumbnail

· European Commission FAQ

The Duty That Wasn't Postponed: Verifying the EC's AI Act Article 50 FAQ

The digital omnibus deferred high-risk obligations, but Article 50 transparency duties took effect on 2 August as scheduled. We verify the European Commission's official FAQ — grace periods, exemptions, and enforcement structure.

  • Four duties, owners and dates verified against the source
  • Only machine-readable marking gets grace until 2 Dec
  • Enforcement sits with national authorities, not the AI Office
The Duty That Wasn't Postponed: Verifying the EC's AI Act Article 50 FAQ thumbnail

· MCP Specification

MCP Without Sessions: A Review of the 2026-07-28 Specification

A review of the MCP 2026-07-28 spec verified against its SEPs — the stateless rewrite, the Tasks extension, and what the Roots/Sampling/Logging deprecations leave on implementers' desks.

  • Session removal and header routing: a redesign fitted to web infrastructure (SEP-2575, 2243)
  • The price of dropping tasks/list: handle persistence and task registers become client duties
  • Caveats stated: self-reported adoption figures, second major redesign in eight months
MCP Without Sessions: A Review of the 2026-07-28 Specification thumbnail

· NBER Working Paper

How Real Are AI Coding Productivity Gains? A Review of NBER w35275

A review verifying three generations of AI coding tools against data from 100k+ developers — and why a +180% commit gain decays to +30% in shipped releases.

  • Marginal and cumulative effects reconcile exactly across all six production layers
  • Only Claude Code (+29.2%) translated into releases — the pipeline, not the tool, is the bottleneck
  • Full reliability assessment: conflicts of interest, contradicting RCT evidence, coverage limits
How Real Are AI Coding Productivity Gains thumbnail