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This paper critiques recent methods claiming abstraction-first learning in large language models, showing that pure exemplar models can mimic abstraction-first learning depending on input distributional properties.
Introduces DE-NER, a dialogue elicitation framework for zero-shot named entity recognition that uses self-play between questioner and roleplayer LLMs to clarify entity boundaries, achieving an average 3.75% F1 improvement over baselines.
Introduces agentic graph token reasoning, a method that recasts graph tokenization as part of an LLM's step-by-step reasoning process, allowing the model to dynamically choose graph views and granularity during inference. The method outperforms baselines across seven graph domains and transfers zero-shot to unseen domains.
Introduces SIGNPOST-Bench, a benchmark for evaluating how multimodal large language models resolve conflicts between text and visual cues, using counterfactual image variants and geolocation as a diagnostic.
This paper introduces SA-OPD, a spurious-signal-aware on-policy distillation framework that filters misleading token-level teacher supervision based on input-grounding and optimization impact, improving LLM and VLM distillation performance.
This paper studies quantization trade-offs for deploying translation models like EuroLLM and Hy-MT2 on a single GPU, showing that combining document chunking with W4A8/W8A8 quantization improves latency-throughput trade-offs while introducing a document-level evaluation to assess long-context dynamics.
This paper surveys and benchmarks NLP-based automatic deception detection in legal contexts, comparing fine-tuned transformers and seven LLMs with various prompting strategies across seven datasets. Results show domain sensitivity, with fine-tuned models excelling in general domains and few-shot LLMs competitive in low-resource legal settings.
This paper investigates using an 8-billion parameter LLM to improve autonomous cyber defense, then distills its policy into a lightweight 64,910-parameter RL agent, demonstrating feasibility across CybORG scenarios.
This paper proposes WILC, a framework for coordinating multiple LLMs through complementarity-driven iterative collaboration, demonstrating superior performance across benchmarks at lower estimated cost compared to GPT-5.2.
This paper studies the LSR-Synth benchmark and finds that a fixed vocabulary covers most tasks, while language-model-generated candidates rarely expand the set of solvable instances except when vocabulary coverage is disrupted.
This Perspective paper argues that large language models are not yet safe for autonomous clinical decision support, particularly in triage of undifferentiated patients, due to lack of robust evaluation under incomplete information and asymmetric costs of missed diagnoses.
PCSD introduces a method for dense token-level supervision in agentic reinforcement learning by using persistent consistency of teacher-favoring signals, improving performance on ALFWorld and WebShop over baselines like GRPO and SDAR.
EdgeRazor is a lightweight framework for compressing large language models using entropy-guided mixed-precision quantization-aware distillation, achieving 1.88 bits per parameter while preserving teacher model competence and requiring no changes to inference implementations like llama.cpp. The method is demonstrated on small models such as MobileLLM and Qwen variants.
Simon Willison's keynote at AI Engineer World's Fair reviews the last six months in LLMs, highlighting over 30 significant model releases and his 'pelican on a bicycle' SVG benchmark as a practical evaluation tool.
Two research groups independently used OpenAI's GPT-5.6 Sol Ultra to help produce proofs for the same unclonable encryption problem, submitting nearly simultaneous arXiv preprints. The near collision illustrates AI's growing role in theoretical computer science and raises questions about independent discovery and credit.
This paper introduces CoMem, a method that exploits the depth-wise division of labor in LLMs to cache intermediate residual tensors and recompute only upper layers for retrieval, enabling bounded read compute and memory independent of stored-context length. Evaluated on Qwen3-8B, CoMem achieves strong long-context performance with significant memory savings and prefill speedups.
Introduces Policy Gradient Steering (PGS), a method that formulates activation steering as a reinforcement learning problem, using policy gradients to construct removable, composable steering vectors from behavioral objectives. Validated in gridworld, chess puzzle, and football environments.
This paper compares human linguists in training, a trained linguist, and LLMs on annotating evaluative language using Appraisal theory, finding that LLMs achieve strong performance and can assist in complex annotation tasks.
This paper introduces LEEPS, a latent-guided explore-exploit prompt sampler for efficient reinforcement learning with verifiable rewards (RLVR) in LLMs. It adaptively balances reuse of informative prompts and exploration of uncertain ones, improving reasoning benchmark scores by 2.6-3.7% over baselines while adding only ~2 seconds of overhead per training step.
RepBench is a benchmark-grounded data layer for representation probing in LLMs, built from 13,427 benchmark papers and 353 public datasets, yielding 46,149 probe texts across 94 capabilities. It evaluates capability vector transfer across models and readout methods.