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CategoryAllAI industry 19AI safety & security 13AI research 9Data breach 4AI policy 4Vulnerability 3AI tools & infra 3Malware 2Industry 2Tools 2Model release 2Advisory 1Phishing & fraud 1Research 1
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Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPost · 2d agoAI research1

Stanford Researchers Release Paper2Agent: Turning Research Papers Into AI Agents That Reproduce Results and Run on New Data

Stanford researchers released Paper2Agent, a Nature-published pipeline that turns research papers into MCP servers agents can execute.

A Stanford team led by Jiacheng Miao and James Zou published Paper2Agent in Nature on 16 September 2026. Built on Claude Code's agent SDK, it converts a paper and its codebase into a Model Context Protocol server with validated tools, resources, and prompts. In benchmarks, the AlphaGenome agent built 22 tools in about 45 minutes for US$14, scored 100% on 15 novel queries versus 78.7% for Claude Code with repository access, and cut median runtime 1.9x. In scale tests, 74 of 100 bioRxiv papers were converted and 593 of 599 proposed tools passed validation.

MarkTechPost · 15h agoAI research1

How much of F-Droid is LLM generated?

A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.

A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.

Hacker News · AI · 2d agoAI researchHN 46↑ · 23 comments

Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering

Evaluation of twelve LLMs on 222 clinical questions shows verbatim quotes rarely substantiate claims; claude-opus-5 fully substantiates only 37.1%.

The authors build a standardized harness over four clinical practice guidelines and evaluate twelve LLMs on 222 synthetic clinical questions, measuring citation attachment, verbatim quote production, and claim substantiation. Most models attach verbatim quotes to over 90% of claims from prompting alone, though lightweight models like claude-haiku-4.5 struggle. Quotes frequently fail to substantiate claims: claude-opus-5 quotes 98.0% of claims but fully substantiates only 37.1%, exposing a capability gap for verifiable clinical QA.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Playing log(N)-Questions over Wikipedia Abstracts: Communication Efficiency Between Paired Frontier Models

Six frontier models play a two-agent log(N)-Questions game; Claude Opus 5 lags with 28/68 wins while the top five are near-tied.

The study evaluates six frontier models on a two-agent game where a questioner must identify one of N Wikipedia lead paragraphs in exactly log2 N yes/no questions, run over 408 games at $363 total API cost. Claude Opus 5 wins 28 of 68 games versus 45-56 for GLM-5.3, GPT-5.6 Sol, Grok 4.6, Gemini 3.8 Flash, and Kimi K3. Pooled top-five win rates decline with set size (r=-0.973) and fit win = p^(log2 N) with per-round reliability p=0.928, and information per question correlates with win rate at r=+0.88.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research 20 sources

Can Skills Learned in Games Transfer to Real-World Work?

Good Start Labs trains models in strategy games like 1830 and Diplomacy, showing terminal-agent training transfers to financial research benchmarks.

Good Start Labs, spun out of Every with $3.6M from General Catalyst and Inovia, trains AI models in verifiable strategy games. A 30B model trained as a multi-turn terminal agent in 1830: The Game of Railroads and Robber Barons improved Finance-Agent benchmark performance, while single-turn QA training did not transfer. The founders also co-authored COS-PLAY, a paper on co-evolving LLM decision and skill-bank agents for long-horizon tasks.

Latent Space · 1d agoAI research

ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks

ProgramDistill is a benchmark evaluating coding agents on reconstructing web app features from reference applications, testing nine frontier agents.

ProgramDistill evaluates coding agents on features discovered through interaction with fully functional reference applications, factorizing apps into features with replayable behaviors verified via gold patches. Its mine-craft-patch pipeline discovered 1,975 replay-verified behaviors across 26 applications and built 4,063 tasks without human intervention. On cumulative full-application reconstruction workflows, GPT-6 Astra achieved 49.2% and Claude Opus 5 28.8% success. In partial reconstruction, success drops from 100% to 64.0% and from 96% to 32% as restoration depth increases from 1 to 8.

Hugging Face daily papers · 1d agoAI research

Before You Poll with LLMs: A Deliberative Diagnostic Framework

Deliberative diagnostic shows all five tested frontier LLMs misrepresent human belief shifts after arguments, with GPT-5.1 reversing on outgroup questions.

The Deliberative Polling Diagnostic Framework compares human and LLM persona belief shifts after identical informational interventions, using data from America in One Room (526 personas, 72 questions). All five frontier models tested failed uniquely: GPT-5.1 exhibited partisan reversal (80% on outgroup vs 26% on policy questions), Gemini 2.0 Flash, Claude Sonnet 4.5 and Llama 3.3 70B overshot at 5-7x human magnitude, and DeepSeek V3 showed near-zero change (rigidity). The authors term the underlying signature 'self-sycophancy', conformity to the model's internal persona stereotype rather than reasoning from provided information.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Why don't machine learning research agents overfit?

Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.

Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.

Hacker News · security · 2d agoAI researchHN 21↑ · 3 comments