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Computation & Language / NLP
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Newest papers, page 17
DiabAgent: Toward Agentic Physiological Control
As pretraining scales, medical language models have advanced rapidly in clinical question answering.ufakzeka-kulak: Building and Evaluating a 31.6M-Parameter Turkish Speech Recognizer from Scratch
ufakzeka-kulak is an open Turkish speech-to-text model of 31.6M parameters, a FastConformer encoder with a token-and-duration transducer (TDT) and a CTC head, trained from scratch and released under…Tracing the Scientific Discovery Capacity of LLMs to Their Training Data
Large language models (LLMs) show growing promise for scientific discovery, yet basic questions remain about what enables reliable discovery.Strategist: Evolving Reasoning with Agents
A model is only half of an AI agent system.D&A: Decoupling Description and Assessment Enables VLMs as Zero-Shot Robotic Reward Models
Vision-Language Models (VLMs) hold promise for zero-shot video task-progress estimation in robot learning, yet existing frameworks frequently suffer from confirmation bias, hallucinating progress in…WebHarbor: Docking Real Websites for Evolving GUI Agent Environments
GUI web agents require realistic and interactive environments that remain stable during both evaluation and training.Rethinking Next-Visit Diagnosis Prediction Beyond Aggregate Metrics: Retrieval--Memory Multi-Agent Adjudication
Next-visit diagnosis prediction forecasts the diagnoses documented at a patient's next encounter, turning retrospective electronic health records into prospective clinical prediction.A Conditional Mutual Information Analysis of Response Variety in Language Models
Large language models provide a variety of responses at inference (generation) that conventionally depend on a temperature parameter.Why Pay for Long Reasoning When Rewrite Do Trick? Causal Effects of Reasoning History Compaction on Future Generation and Agent Cost
Reasoning substantially improves language models' ability to solve difficult problems, but long reasoning traces make these gains expensive.EvoGroup: A Self-Evolving Harness for Group Agents in Shared Contexts
With the rise of Group Agent such as Claude Tag, agents increasingly serve as digital workers for long-term team collaboration.EpiLoRA: Fusing Many LoRA Adapters into Low-Precision GEMMs for Serving and RL
Low-rank adapters (LoRA) let one base model serve many fine-tuned variants: multi-tenant engines keep a single copy of the base and batch the requests of many tenants, each with its own adapter.Balancing Chain-of-Thought and Answer Supervision for Small Language Models on Medical Question Answering
Small language models (<=9B parameters) can support local medical question answering (QA) while reducing privacy and latency concerns, but effective fine-tuning remains challenging.Must AI Spell It Out? Pragmatic Comprehension of Alerts in a Time-Critical Task
Assistive systems typically tell users everything, often spending time and attention on what a user already knows, leaving less for what happens next.SLATE: A Large-Scale Benchmark for Long-Horizon API Selection in E-commerce Tool Use
Enterprises increasingly want LLM agents to automate standard operating procedures over their software stacks -- mapping each step of a procedure to the right call among thousands of internal APIs…Thinking Fast, Trading Late: System One Decision Models vs the Market
Large language models (LLMs) read company news at scale, and backtests suggest that their readings predict stock returns.FlagRot: Selective Rotation for Low-Bit KV Caches
Long-context inference is bound by the KV cache, which 2-bit codes could shrink about sixfold if their error stays in check.Whose startup does the AI back? An algorithm audit of reputation and demographic signals in LLM-assisted crowdfunding
Large language model (LLM) assistants increasingly advise backers on which crowdfunding campaigns to support.Dory1.0-4B: A Looped Normalized-GPT
We introduce Dory1.0-4B, a 4-billion-parameter looped (latent-recurrent) language model with elastic depth: a single checkpoint serves different inference compute budgets.WASP-Mem: Let Agent Organize Procedural Memory. Human Spend Effort on Diagnosis
LLM agents struggle to automate complex enterprise procedures because static, pre-built harness cannot capture proprietary policies, undocumented behaviors, or state-dependent recovery paths that…Language Model Augmented Decision Transformer for Multi-Task Offline Reinforcement Learning
Offline multi-task reinforcement learning (MTRL) aims to learn a unified policy from heterogeneous offline datasets that generalizes across tasks.