Research repositories

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.

LongPIBench introduces a benchmark for prompt-injection attacks and defenses in four document-centric long-context workflows: paper peer review, resume screening, email summarization, and code review. It combines synthetic and real-world datasets, evaluates heuristic and optimization-based attacks across eight language models, and measures both prevention-based attack success and detection-based false-positive and false-negative rates. The paper reports that attacks remain effective and that defenses that perform well on short-context benchmarks often degrade on long documents, with additional ablations over document format, injection position, attack goals, context length, and segmentation.

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 introduces LoGo, a decoder-only Transformer attention mechanism that gives every token a local causal-attention branch and selectively activates a full-context branch through a learned token-level gate. An adaptive threshold controls the global activation ratio without an auxiliary balancing loss, progressive masking stabilizes training, and query-sparse Triton kernels avoid computing global attention for unselected queries. Experiments compare LoGo with full-attention and static local-global hybrids across model scales, long-context extension stages, recall benchmarks, kernel runtimes, ablations, and routing analyses.

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.