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Computation & Language / NLP
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Newest papers, page 32
IndicIPR-QA: A Benchmark for Extractive Question Answering on Indian IPR Laws
Legal Question Answering (QA) over statutory texts is a challenging task in Natural Language Processing (NLP), especially in low-resource settings with complex legal systems.Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese
Building a language model of one's own no longer requires a data centre.On the Fundamental Limits of Prefill-Based Decoding Length Prediction
Predicting how many tokens a large language model (LLM) will decode, using only prefill-phase hidden states, is a key primitive for inference scheduling.Ramanujan Challenge Results: AI-Assisted Evaluation Pipeline for Multi-Format Proofs
As AI's mathematical capabilities rapidly improve, meaningful benchmarks are shifting from university-level problems and math olympiads to research-level mathematics.Comparing Human Role-Players and LLM-Simulated Clients in Online Counselling Training: An Analysis of Counselling Patterns
This study investigates the capabilities of Large Language Models to simulate counselling clients in educational role-plays in comparison to human role-players.Every Token Needs Its Own Teacher: Multi-teacher Adaptive On-policy Self-distillation
On-policy Self-distillation (OPSD) has proven effective for post-training large language models by constructing a teacher conditioned on privileged references.PIVOT: Progressive Intervention via Online Teacher Correction
Reinforcement learning from verifiable rewards (RLVR) is limited by sparse outcome-level feedback on hard reasoning tasks: when all rollouts in a group fail, rewards provide no learning signal.Multi-View Latent MoE: Letting Every Router Shape Expert Computation
Sparse mixture-of-experts (MoE) models expand parameter capacity by activating a small subset of experts per token.VeriCUA: Verification as Agentic Exploration for Computer-Using Agents
Building and scaling computer-using agents (CUAs) across desktop, mobile, and web environments requires reliable task-completion verification for data curation, evaluation, and reinforcement learning.When to Assist and What to Suggest: Joint Gating and Memory-Grounded Hint Generation in One Compact Decoder
Proactive assistants offer short, memory-grounded hints during a conversation, deciding when to assist and what to suggest.TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races
We present TextSeal2, a distortion-free watermark for large language models that keeps its signal under speculative decoding: tokens that replace rejected drafts remain watermarked with the same…Testimony from Timelines: Evidence-Grounded Significance Testing of Temporal Clinical Hypotheses
Ask a language model whether sales tend to dip after a product recall, or whether one side effect tends to follow another after a vaccine, and it answers in a fluent sentence that reads like a…ShallowDefend: Harnessing Shallow Safety Alignment for Multi-Turn Defender
Safety alignment has enabled large language models to reliably refuse harmful requests directly, yet the same request distributed across a multi-turn conversation still succeeds far too often.Elucidating the Design Space of LM Data Attribution
Data attribution methods for Language Models (LMs) are typically evaluated using downstream tasks like fact retrieval.Li$^{2}$SAO: Lifting Light Single-Rollout Asynchronous RL Optimization via Group-Relative Actor-Critic Bootstrapping
Asynchronous reinforcement learning (RL) is becoming a mainstream paradigm for post-training large language models (LLMs), as it improves training efficiency by updating the model as rollouts arrive.Apriel Voice 27B Preview: An Open Model for Speech, Reasoning, and Tool Use
A capable voice assistant must do more than recognize words; it must understand requests, reason over context, sustain conversations, and act through tools.Prevented is Not Protected: What Attack Success Rate Hides about RAG
Retrieval-Augmented Generation (RAG) is vulnerable to knowledge poisoning, where adversaries manipulate retrieved evidence to influence model outputs.Mage-VL-Cowork: A Full Training Recipe to Learn Hybrid Computer Use From Evolved Harnesses
Effective computer-use agents (CUAs) should seamlessly combine visual GUI interaction with code and terminal execution within a single policy.Safin-1.5: Safety-Native Architecture and Verifiable Reasoning for Recursive Safety Improvement
Recursive self-improvement (RSI) enables large language models to advance their capabilities through successive updates, but these updates can also introduce new risks and weaken existing safeguards.The Generative Gap Between Speculative Drafter and Continuous Diffusion Language Model
Continuous diffusion language models (CDLMs) and speculative drafters offer efficient language generation but are typically evaluated differently: CDLMs generate independently through iterative…