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Computation & Language / NLP
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PACE: Periodic and Channel-Aligned Encoding of Four-Bit Recurrent States
Linear-attention language models keep their context in a recurrent state of fixed size, where an inference system stores one copy of this state for every sequence it decodes at the same time.WRDO: Weighted Refusal Direction Optimization in Aligned Language Models
Aligned large language models are trained to comply with benign requests while refusing harmful ones.A Diagnostic Study of Faithfulness in Diffusion Language Models for Table-to-Text Generation
Diffusion language models generate by iteratively denoising a masked sequence under bidirectional attention, without the strict left-to-right commitment of autoregressive decoding.Overview of CHIP 2026 Shared Task 1: Patient-Specific Phenotype Concept Recognition in Full-Text Biomedical Literature
Automatic phenotype information extraction plays an important role in rare disease diagnosis, genotype-phenotype analysis, and precision medicine.LLMs as survey respondents: Comparison to simple semantic baselines
Large language models (LLMs) have been explored as surrogates for human survey respondents.Effects of Model Scale and Quantization on Social Bias and Abstention in Question Answering
The deployment of small and quantized language models in resource-constrained settings raises an important fairness question: when a compressed model produces fewer biased responses, does this…SPARE: Sparsity Allocation via Routing Entropy for Efficient MoEs
Mixture-of-Experts (MoE) architectures scale model capacity efficiently, yet their substantial memory footprint poses a major barrier to deployment.Conflicted Records: Typed Adversarial Contracts for Auditable LLM Output Quality Enforcement
LLM-generated records increasingly populate production information systems where they are treated as data assets subject to the same quality expectations as records from operational databases.Uni-Mem: Learning a Long-Horizon Agentic Memory Manager with Unified Online Policy Optimization
Long-term agent memory is shifting from predefined workflows toward learnable policies for memory construction and retrieval.Scaling Multilingual Pretraining of Pixel Language Models
Pixel language models process text as rendered images instead of a finite vocabulary, which gives them strong cross-lingual transfer abilities.Retrieval Augmented Generation Compositionality
Retrieval Augmented Generation (RAG) grounds large language models in external knowledge.Accept or Repair: Preserving Passage-Supported Answers in Contrastive Decoding
Large language models often answer from knowledge learned during training, even when a provided passage contradicts that knowledge.Bridging Foundational and Agentic Capabilities of LLMs
Large language models (LLMs) are increasingly deployed as agents, yet it remains unclear how their foundational capabilities translate into agentic capabilities.ALIAS: Evaluating Performance and Memorization in LLM-Written TLA+ Specifications
Memorization inflates the benchmark scores of large language models (LLMs), since a model can reproduce answers it saw in training.Evolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting
Most contemporary artificial intelligence (AI) systems are released with frozen parameters and improve through external updates, whereas an ideal system would keep improving from its own experience…Where Do LLMs Learn to Recommend AI?
Asked what to study, which career to pursue, or what product to build, large language models (LLMs) often recommend AI-related options.MAPS: Macro-Adaptive Parallel Reasoning for Regulating Search Momentum in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) improves the performance of Large Language Models (LLMs) by retrieving external knowledge.Real PMIDs, Wrong Papers: Harness Design for Biomedical Claim-Verification Agents
Literature-searching language agents are becoming a standard component of scientific assistants for the life sciences, yet there is little controlled evidence on which parts of their harness matter.MemoryTax: The Bitter Lesson of Agent Memory
Agent memory systems such as Mem0 and Zep decide what to keep before any question arrives; coding agents keep the raw history and let the model search it with its own tools.S-EDL: Eliciting Self-Evidence from Sequence Likelihoods for Semantic Calibration of LLMs
Calibrating large language models (LLMs) in open-ended generation is uniquely challenging because many distinct token sequences can express the same underlying meaning.