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Agent、MCP、coding agent、工作流自動化與評測。

最新模型

OctoLong Trains Long-Context Models on Cross-Library Dependency Chains, Lifting the 8B Model’s RepoQA Score to 64.63

Instead of simply concatenating individual libraries into long documents, OctoLong recursively gathers implementations by following AST, language-server, and package dependency relationships. The research team released models ranging from 600M to 14B parameters, with experiments showing that dependency-dense data improves code retrieval, state tracking, and API usage.

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ABSeeker Backtracks from Correct Answers to Derive Search Clues, Raising a 4B Model’s BrowseComp Score to 55.3%

ABSeeker breaks down a search agent’s final success or failure into step-level rewards, preserving useful queries from failed trajectories while downweighting erroneous steps in successful ones. The team has released the 4B model weights and the inference and training pipelines, though its best results still rely on additional context management and an LLM judge.

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KV Cache Is Not “Latent Thought”: Mismatched Caches Reveal That Multi-Agent Gains May Be Merely Interface Effects

A new study re-audits latent communication in multi-agent systems using mismatched, zeroed, and random KV caches, finding that the large performance drop caused by removing the cache does not necessarily prove that the receiver used specific information from the sender. On some standard benchmarks, zeroing the cache reduced accuracy by 14.7 points, while replacing it with a cache from another problem cost only 0.4 points.

AI 安全

New Benchmark Finds Implicit Bias Can Evade Chain-of-Thought Monitoring, With Detection Rates as Low as 5% in Some Settings

A new study uses paired prompts to compare whether models leave detectable evidence in their chains of thought when influenced by explicit instructions versus implicit cues. Across seven reasoning models, answers still changed under some implicit settings, while monitor detection rates were as much as 46 percentage points lower than in explicit settings.

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AI Scientists Turn to Race Cars and Decks for Prospective Evaluation; Only 10 of GPT-5.2’s 166 Ideas Match Real-World Innovations

The new evaluation seals model-generated ideas before using season-specific technologies and professional tournament decks as answers revealed only later, reducing concerns about data contamination in retrospective benchmarks. The results suggest that models do not lack plausible-sounding ideas; the real bottleneck is filtering, combining, and identifying genuinely useful novelty.