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By avoiding weight updates entirely, Nullify serves as an efficient, plug-and-play inference-time intervention framework for LLM unlearning, and matches or surpasses established baselines in forget quality while achieving near-lossless preservation of model utility.
Results show that preserving already-correct work under unsupported accusation is a distinct safety challenge for long-lived agents, and introduces CAVE-Bench, a benchmark of 365 agentic tasks across six domains built around opaque tasks.
Verifiable rewards improve language models through reliable task-level feedback, but methods based on Group Relative Policy Optimization (GRPO) apply a sequence-level advantage uniformly across all tokens. This coarse credit assignment reinforces or penalizes entire responses without identifying which local decisions t...
Prospective Multimodal Memory Compilation is proposed, a framework that shifts part of the memory reasoning process from query time to memory consolidation time and improves answer quality and visual evidence recall while reducing query-time token and latency costs.
Tempered Evidence Fusion (TEF) is proposed, a training-free rule that weights each sentence's log-odds by its normalized information gain, as derived from a generalized Bayesian posterior, making the fused score nearly vanish for uncertain sentences while preserving the Bayes-optimal weight of decisive evidence.
Ro is proposed, a rubric-guided process reward framework for stepwise model routing that consistently outperforms strong baselines and achieves better accuracy and cost trade-offs.
Results suggest that AAPT performs best when candidate actions can be enumerated in advance, whereas reactive execution remains stronger when they cannot, and on an external benchmark, AAPT matches the overall performance of a reactive baseline, although the two methods exhibit complementary strengths.
A three-tier diagnostic taxonomy shows that verification/feedback and planning failures dominate execution/grounding errors, while a single scalar success rate can not explain, and connects these findings to newer long-horizon CUA benchmarks and derive stage-specific design rules for CUA evaluation.
The Personal Agent Sycophancy Benchmark (PASB) is introduced, a 1,600-task benchmark that traces whether a conversational claim is accepted, written into durable agent state, and reused in a later neutral query, and shows that agent sycophancy is fundamentally a state-writing governance problem.
Experiments on long-horizon embodied and web-agent benchmarks show that MemPrism consistently improves the task performance, especially as trajectories become longer, while reducing memory token consumption.
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