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Computation & Language / NLP
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Newest papers, page 62
SWE-Clar: Can Coding Agents Clarify Incomplete Client Requirements and Deliver Greenfield Software?
Greenfield software development in freelance settings requires coding agents to turn incomplete client requests into working software.Aligning Large Language Models with Human Feedback: Mathematical Foundations and Algorithm Design
This article provides an introduction to the mathematical foundations and algorithmic frameworks used to align Large Language Models (LLMs) with human intentions, preferences, and values.Machine-Readable Operator Manuals over the ISO 11783 Network for Offline, Multilingual LLM Assistance
If agricultural machinery is to support intelligent, offline assistance, then the system must access technical documentation dynamically.Rewriting Scaling Laws for Synthetic Data with Compute and Data Constraints
Synthetic rewrites, where a language model rewrites a natural document e.g. into a target style, are now a standard ingredient of pretraining corpora.Mining Frames: Association Rule Mining as an Unsupervised Framework for News Framing Detection
Computational framing analysis is dominated by supervised classifiers that require large annotated corpora and predefined frame taxonomies, limiting their use for non-English languages, novel…TaxoSAE: Rethinking Sparse Autoencoders through Taxonomic Logic
Recent Sparse Autoencoders (SAEs) decompose the representations of large language models (LLMs) into interpretable features, enabling precise readout and control of encoded knowledge.Knowing When to Stop: The Evidence-Action Gap in Coding Agents
Coding agents are trained to complete tasks, and they keep trying even when the environment makes completion impossible.LazyFlash: Forward-Dense, Backward-Sparse Attention for Accelerated Long-Context Fine-Tuning
As large language models (LLMs) process increasingly long sequences, attention becomes a major computational bottleneck.Agreement rather than normalisation keeps depth reusable in looped language models
Looped language models apply one block of layers repeatedly, and the design pays off only if depth is reusable: loop steps beyond the trained budget must remain usable at inference.Kolibri: A Sovereign European Model on the Pareto Frontier
This report introduces Kolibri, an English-German mixture-of-experts transformer with 78.1B total parameters and 3.46B active parameters per token, released as open weights under the Apache 2.0…Which Answer Did the Seventeen-Year-Old Write? A Behavioral Case Study of Author-Age Misattribution, Miscalibrated Confidence, and Non-Corrective Self-Explanation in Six Frontier Language Models
Three students answered the same exam prompt by hand: "What is happiness?POS-Signature Demonstration Retrieval for In-Context Dependency Parsing
Demonstrations for in-context learning are often retrieved by semantic similarity, which may be a poor proxy for relevance in structured prediction.Knowing Whom to Trust is not Enough: The Trust--Agreement Gap in Repeated Multi-Party LLM Negotiation
Large language model (LLM) agents increasingly participate in negotiations on behalf of individuals and organizations, yet they remain vulnerable to manipulation, particularly in repeated…The Helpful Agent Problem: When Emergent Cooperation Among LLM Agents Bypasses Intended Workflows
LLM agents are increasingly deployed in collaborative settings where they pursue separate tasks while sharing resources and operating under review or oversight processes.AusClinBench: a deterministically scored benchmark of large language models against current Australian clinical policy
AI models already inform clinical decisions and draft notes in Australian clinical software, usually without the practitioner being told which model is used.DraMaze: A Multi-Agent Pipeline with Future-Aware Memory for Diverse Microdrama Script Generation
The rapid growth of microdramas has increased demand for high-quality script production.Prospect Theory in Large Language Models: A Meta-Analysis of Loss Aversion, Diminishing Sensitivity, and Probability Weighting
Prospect theory splits risky choice into three parts: loss aversion, measured by the relative weight of losses to gains, $λ$ (e.g., $λ= 2$ implies that losses weigh twice as much as gains)…VeriEvolve: Evaluator-First Evolution for Tool-Using Agents
A central obstacle to self-improving agents is the absence of a reliable fitness signal for realistic stateful tasks.Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding
Speculative decoding accelerates large language models by verifying drafted tokens in parallel.Our Own Jev: Typed Decisions in One Forward Pass, from an In-House Judge to Open LoRA Models
A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a language model's logits or a small head over its…