研究仓库

Structured state space models (SSMs) and attention mechanisms have evolved largely as separate sequence modeling paradigms. This work introduces Structured State Space Duality (SSD), establishing theoretical connections between structured SSMs and attention variants through semiseparable matrices and dual contraction representations. Exploiting this duality, the authors develop a block-decomposed matrix multiplication algorithm that computes 1-semiseparable selective SSMs utilizing matrix multiplication units on modern accelerators. Incorporating SSD into a refined parallel neural network block yields Mamba-2, which achieves substantial compute throughput improvements over Mamba-1 while matching or outperforming Transformers on autoregressive language modeling benchmarks.

Knowledge distillation with post-trained teachers is increasingly used to compress language models, yet whether its benefits remain consistent across training stages is unclear. Evaluating logit-based distillation on OLMo-2, the authors identify a stage-dependent reasoning-recall tradeoff: while forward-KL distillation improves both reasoning and factual recall during pre-training, it slows factual acquisition during mid-training. This discrepancy stems from asymmetric teacher confidence across domains—teachers are confident on procedural tasks but uncertain on factual tokens, which students learn early. To resolve this imbalance, the authors introduce Switch Distillation, dynamically routing confident tokens to reverse-KL distillation and uncertain tokens to cross-entropy. Switch Distillation matches or exceeds baselines across reasoning and knowledge benchmarks while preserving factual recall.

Skill-augmented agents persist loaded skills across execution loops, creating severe vulnerabilities where malicious or compromised skills steer tool calls, leak secrets, or corrupt state after installation. Static pre-installation vetting cannot detect these task-conditioned threats. To address this challenge, the authors introduce Defense-as-Skill, deploying the runtime guard as an installable, inspectable, and editable skill named SkillSonar that dynamically gates sensitive actions through allow, replan, or confirmation decisions. They construct SCOPE-R, a benchmark spanning six risk families with 206 attack-confirmed malicious instances and 43 benign tasks, and optimize SkillSonar using feedback-driven Monte Carlo Tree Search. On repeated GLM-5 evaluations, SkillSonar substantially curtails attack success while preserving benign task utility across multiple agent harnesses.

Standard supervised fine-tuning (SFT) treats all expert demonstrations identically, allocating gradient updates uniformly regardless of how well the model has already mastered each query. Online Self-Weighted Fine-Tuning (OSW-FT) addresses this limitation for binary-verifiable reasoning by dynamically rescaling the supervised fine-tuning loss using an online pass rate estimated from a small number of rollouts. By anchoring the optimization direction to high-quality expert trajectories while modulating update magnitude using an empirical advantage weight of one minus the estimated success rate, OSW-FT concentrates gradient steps on unsolved queries. On the Qwen3 model family across challenging benchmarks including AIME, AMC, MATH-500, and GPQA-Diamond, OSW-FT consistently improves over standard SFT with only two rollouts per query.

LLM agents need to sustain goal-consistent reasoning across long multi-turn interactions under strict resource constraints. However, as the multi-turn context accumulates, it can destabilize the underlying LLM's internal representation of task-relevant information from earlier turns, blurring the boundary between constructive reasoning and representation drift. We formulate multi-turn reasoning as a hidden-state trajectory of the underlying LLM that is characterized via two complementary signals: temporal curvature that captures the directional consistency of turn-to-turn updates, and variance slope which measures the expansion or contraction of the exploration space. Across four tasks and three underlying LLMs, we observed that these geometric signals distinguish between correct and incorrect episodes prior to completion. We further decompose each episode into three-action chains formed from four actions (Read, Write, Respond, Transfer) and show that separability is action-dependent, with different signals distinguishing various chain patterns. Our experiments demonstrate that trajectory geometry can identify critical turns in the reasoning process, increasing task success rates on tau-Bench from 24.1% to 39.6% while reducing token cost by 11.2%.

The paper presents AGENTIC R AG-R1, a reinforcement-learning framework for retrieval-augmented multi-step reasoning. It combines fine-grained actions, stack memory with push, pop, revision, and summarization, hierarchical outcome and process rewards, and information-aware rollout rejection. Experiments report comparisons, ablations, step-budget scaling, model-size effects, timing, and downstream agent evaluations.

The paper introduces DoCtOR, a reflection framework for large-language-model multi-agent systems. DoCtOR uses a process-reward-based ProFA diagnosis module to identify the first incorrect reasoning step and responsible agent in a failed trajectory, a counterfactual correction module to propose and score an alternative action, and a reflector that gives targeted feedback to the decisive error agent. The authors evaluate the framework on HotPotQA, ChartQAPro, and Mind2Web, compare it with Reflexion, Retroformer, and COPPER, and separately evaluate ProFA against automated failure-attribution baselines on held-in and held-out Who & When data. Additional experiments study model size, test-set size, trajectory scope, correctness threshold, and component ablations.