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Research briefings and fact-rooted posts - what the papers, HN threads, and benchmarks actually say.
62 resources - 57 posts, 1 tool, 4 guides

The text agents read is a control surface: bad MCP error messages cost more on stronger models, and context hygiene gains value with every model upgrade.

Approve effects, not invocations: why agent approval records miss harm in install hooks and skill combinations, and why pipelines need closure metrics.

CodeMidas turns existing repository code into executable coding-agent RL tasks, tests, and verifiers, instead of relying on issues, commits, or hand labels.

An agent's report on its own run is unreliable, and eight studies show why. The fix: append-only traces, obligation ledgers, and records the agent cannot write.

AI scribe audits show omission is the dominant error, and LLM judges are near coin-flip at catching it. The fix: enumerate the facts, then check each one.

Coding agents need better human loops: a position paper argues agent research over-optimizes solo autonomy while developers need to steer and verify agents.

Agent instruction following collapses past five or six simultaneous constraints. Why rules also die in compaction and handoffs, and why side channels help.

AI agents often claim completion they did not earn. New benchmarks measure the gap, and evidence-carrying termination makes done a checked artifact.

Agent model compression has a hidden tax: quantized and pruned agents lose knowledge first and stay confidently wrong. Why cheaper agents start at the cache.

Self-improving test generation loops can grade against an oracle that agrees with itself. An audit shows the inflated gain and why independent checks count.

LLM judges flip verdicts under pushback and rankings shift with token budget. The design spec for a judge system, including cheap deliberating juries.

DeepSeek Harness (dsh) code read: a 453K-line plugin runtime on a vendored Cordis fork, three patterns worth stealing, and the gaps the launch does not mention.

A new August 2026 paper argues that coding agents improve faster when they compare attempts across tasks and lineages, not just retry one failed trajectory.

Skill files are a supply chain attack surface: adversarial skills exploit a mainstream coding agent in over 95% of runs. The fix is admission engineering.

ACE vs ALTK-Evolve for agent memory: both turn trajectories into lessons, but calibrated delivery matches accuracy on AppWorld at a fraction of the tokens.

Encrypted chain-of-thought from Anthropic, OpenAI and Google APIs can be replayed into weaker models and read verbatim. How the attack works and what leaked.

A new arXiv paper probes six widely used open-source agent frameworks and finds the barrier semantics of approval gates, cancellation, and timeouts hold on none of them. A sibling branch can execute while the user is rejecting another one, and replay can double-execute. The fix is a verified external gate called SoundGate.

AgentChaos injects server errors, truncated responses and corrupted tool calls into live agents. Pass@1 drops up to 50 points: robustness is a systems problem.

LivePlan monitors SWE-agent runs and calls an advisor LLM only on drift, raising resolution rates up to 15.2 points for an extra $0.08 per instance.

WeatherNext Cyclones, DeepMind's open-source tropical cyclone model, adds a day of forecast lead time. What the Nature paper shows and how to run the weights.

Fine-tuned coding agents collapse when you switch scaffolds, from 52.6% to 8.4% on SWE-bench Verified. DCAS shows how to train planning into the model.

TutorMoments, AI2's replay-based benchmark for LLM tutors, finds models over-help by default. How it scores scaffolding and rigor in real tutoring transcripts.

SkillSV is a Shapley framework that values the rules, examples and scripts inside an agent skill, cutting skills to 69% of tokens without significant loss.

A benchmark plateau can be a broken yardstick: a SciCode audit found 263 defects, and corrected scores jump to 84-98 percent. How to read flat leaderboards.

Kill your agent runs early: production traces show context goes stale at turn boundaries, and the fixes that moved numbers are kill, carry the state, restart.

Copilot traces from 3.2M users and 761M LLM calls: 87% of calls are agent-initiated, KV cache hits drop at turn boundaries, tool failures multiply compute.

The LLM judge is leaving the agent loop: evidence gates, verifiable rewards and deploy-time certificates replace verdicts with structure. Here is our bet.

Agent benchmark noise is being fixed by architecture - ledgers, counterfactuals, decomposition, personas, readout discipline - not by smarter judges.

Agent benchmarks are noisier than their scores admit: audits found misaligned ground truth, lenient model judges and scalars that hide failures. What to trust.

Agent memory is moving into the model: in-backbone memory, parametric memory, reconstruction and transactional writes challenge external stores. What to do now.

Blind resampling beats self-repair in small code models: a placebo-controlled MBPP+ study finds retrying from scratch beats feeding failed code back.

CAPA benchmark: coding agents that read a user's past sessions resolve ambiguous requests with far fewer clarifying questions, tested across 12 models.

Change2Task turns merged pull requests into verified, executable coding agent tasks that stay current with the codebase, across five task families.

DeepSeek V4 Flash 0731 was re-post-trained for agent work. Its benchmark numbers, native Responses API and Codex support, and how to wire it up today.

Microsoft's study of Claude Code and GitHub Copilot CLI found about 24% more merged PRs among adopters. The bigger lesson is rollout design, not magic.

ORCA-bench puts five frontier coding agents on live oncall root cause analysis with real telemetry. The best scored 10% on hard tasks, far from oncall-ready.

OwlPath encodes code into an OWL2 ontology so coding agents can answer multi-hop structural queries with SPARQL, cutting tokens 28.8% on SWE-bench Pro.

PAIChecker finds 13.6% of SWE-bench Verified instances pair a PR with an issue it does not resolve, penalizing agents that solve the stated problem.

SIGIL compiles prose SKILL.md files into typed agent harnesses. Prose runs execute 56% of mandated steps; compiled harnesses reach 86% with 0.58x the tokens.

SWE-NFI benchmarks coding agents on 188 behavior-preserving improvement tasks. The best hits 70% correctness, but all lag humans on structural refactors.

What if AI was free tomorrow at today's capabilities? A thought experiment on what changes for developers when price hits zero, and what stays scarce.

A $500 GRPO fine-tune of Qwen 3.5 9B beat GPT-5.6 Sol and Opus 4.8 on catalog review, hitting 97% accuracy at a fraction of the cost per listing.

FLUX 3 from Black Forest Labs is one multimodal model for image, video, audio, and robot actions. What it does, how it works, and how to try it.

Claude Fable 5 found a counterexample to the 87-year-old Jacobian conjecture, and Terence Tao worked through the math. How it works and what HN is saying.

Dockerless, ByteDance's environment-free verifier for coding-agent patches, explained - why it complements CI as a cheaper evidence stage before full test runs.

Ghost Font hides text in video using motion, so humans can read it but frame-by-frame AI models cannot. How it works and how HN commenters broke it.

Harness engineering is where self-improving AI starts, Lilian Weng argues, not models rewriting their weights. What that means for developers building agents.

Ilya Sutskever's rumored 30-paper ML reading list, now on 30papers.com. What the papers cover, the compression thesis behind them, and what HN thinks.

A Dartmouth study of an AI tutor in intro statistics links full engagement to a 0.71-1.30 SD exam gain. What it measured, and why selection bias still matters.

A controlled study of 660 Claude Code trials shows clean codebases reduce token usage by 7-8% and file revisitations by 34%, while pass rates stay the same. Traditional maintainability principles still matter in the age of AI coding.

Does code cleanliness affect AI coding agents? A SonarSource study finds the same pass rate, but 7-8% fewer tokens and 34% fewer file revisits on clean code.

Program-as-Weights compiles prompts into compact local adapters that act as fuzzy functions. What the paper claims and where developers could use it first.

Claude Science wraps Claude with scientific tools, local code execution and HPC access. How to get it, what it costs, and how it fits alongside Claude Code.

Prompt injection is role confusion, per an ICML 2026 paper: LLMs judge who is speaking by style, not role tags, so text that sounds like the system wins.

Multi-Stream LLMs argue agents are bottlenecked by single chat streams. The practical takeaway: design agent runtimes around separated channels now.

A trending refusal-direction paper is a reminder that model safety cannot be treated as a thin refusal layer. Builders need layered controls around the model.

Over-editing is when AI coding agents make huge diffs for tiny fixes. Why models do it, why tests miss it, and how to keep agent edits minimal.
Set up Codex Chronicle on macOS: enable it, grant Screen Recording and Accessibility access, and understand data storage, privacy, prompt injection and fixes.
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