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GitHub 熱門專案、開源工具、框架、runtime 與重要 release。
代理工具
DeepWeaver uses a Thought Block Chain to organize claims, key information, and sources, then repeatedly checks for evidence that has not yet been used. It improves long-form responses and citation metrics across several models, but the new benchmark focuses on Chinese-language water environment questions, and the code has yet to be released.
推論系統
Liquid AI has released three DSpark draft models with roughly 300 million parameters, enabling LFM2.5 to verify multiple candidate tokens at once through speculative decoding. Tests with batch size 1 and greedy decoding showed speedups of up to 3.18×, but SGLang support remains in an open PR, leaving deployment maturity yet to be validated.
代理工具
Before real-world issues arrive, SkillForge creates verifiable failures from execution paths covered by tests, then distills repair trajectories into project-specific skills. When paired with Mini-SWE-Agent, it raises SWE-bench Verified Pass@1 for DeepSeek-V3.2 and GPT-5-mini to 72.2% and 60.6%, respectively.
最新模型
Open-MOPD finds that the main bottleneck in multi-teacher online policy distillation is not conflicting teacher gradients, but a severe imbalance in the effective number of token updates allocated across domains. By combining token-share balancing, gap-aware allocation, and student reward refresh, it recovers 83.4% of the available capability gains in a single 3B model.
科學機器學習
Monroe combines sparse graph attention, stereochemical edges, and 1,152 pretraining objectives, then uses TabPFN for in-context prediction on frozen molecular embeddings. It ranks in the statistically best-performing group across all 28 Polaris tasks, although its “100% win rate” includes ties in which it is not significantly different from the best method.
代理工具
SPADE trains the environment designer using the return gap between runs with and without hints, allowing the curriculum to become harder automatically as the agent improves. Qwen3-30B-A3B gained as much as 13.9 points on tool-use benchmarks, though environment validators may still accept underspecified or unsolvable tasks.
硬體
VSPG stores each action as a unit hypervector, reformulating the softmax policy gradient as advantage-weighted vector superposition and normalization, without requiring automatic differentiation for the actor or optimizer state. It achieves higher returns and greater resilience to bit errors in two building-control climates, but performance depends heavily on the fixed encoder.
代理工具
Using 26 tools and roughly 340 to 400 decision points, FM-Bench tests whether agents can handle delayed rewards, hidden information, and competitors’ reactions. All 15 models completed the 20-year campaign, but rankings stabilized only in the later stages, while self-managed memory exhibited two failure modes: endless accumulation or repeated overwriting.
最新模型
Researchers repeatedly update hidden states through the same set of Transformer layers before generating each token, comparing recurrent and conventional models using identical data and LoRA configurations. Gains are concentrated in parallel calls and cross-call dependencies, while API-Bank, which primarily involves single API calls, shows no consistent advantage.
AI 安全
A new study links continuous vectors exchanged between agents—but omitted from conversation logs—to their subsequent public actions as a single event, then applies three layers of checks: anomaly detection, counterfactual impact analysis, and sparse-feature inspection. The average AUROC of 0.993 combines text-based and latent-collusion samples, and the attacks were predesigned by the researchers, so the result cannot yet be considered a general capability for detecting emergent conspiracies.
代理工具
FACET binds instructions, containers, reference solutions, and verifiers to the same execution state, synthesizing 6,078 verified tasks from more than 71,000 agent skills. Fine-tuned on just 1,200 successful trajectories, Qwen3.5-9B improved its Terminal-Bench 2.1 score from 27.34 to 35.58.
代理工具
The study finds that although agents can independently train, debug, and evaluate models, they lock in their training strategy before the first parameter update. Adding experiment logs, a skill library, and an evaluation agent improves downstream scores but still does not prompt systems to change approaches when the evidence turns against them.