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Newest papers, page 149
EASE: Entropy-Adaptive Distribution Shaping for Evading AI-generated Text Detectors
AI-generated text (AIGT) detection can be sensitive to the decoding choices of the source large language model (LLM).Sensitive-Topic Leakage Through LLM Routing Metadata: Measurement and Mitigation
LLM routers pick a cheap or expensive model per request by its content, and many gateways and some cloud platforms can log that choice with content logging off.The Long Road to the Same Answer: Cognitive Bias Under Escalating Reasoning Budgets in Large Language Models
Reasoning models allocate extra computation at inference time and present their answers as the product of deliberate thought.Cache the Encoder Within:Compact, Reusable Memory across LLM Queries
Repeated queries over shared documents incur redundant encoding, while caching model states introduces persistent storage costs.SkillSandbox: Skill Verification via Dynamic Scenario Synthesis
Self-evolving agents distill task-solving experience into skills for future reuse, but these skills can encode incorrect procedures or non-transferable knowledge.ExperienceIndex: Artifact-Grounded Memory
Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature).I would rather quit NLP than read another paper like this: The rise of antithesis in NLP papers
For better or worse, LLMs are by now used routinely for scientific writing.\footnote{This paper is no exception; we did use AI to assist with writing some of the sections (see Acknowledgments).}…Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid Position
The architectural design of Large Language Models (LLMs) is shifting from traditional full-attention-only models to hybrid models, which combine different attention modules to improve long-context…YANchor-4B: Effective Long-Horizon Reasoning in O(N) Time with O(1) Memory
Long-horizon reasoning demands access to earlier information at a manageable generation cost.LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction
Predicting the future impact of a newly published paper is challenging because it must be inferred from heterogeneous evidence available at publication time.InterView-C: A Synchronized Multimodal Corpus of VR Avatar-Mediated Survey Interviews
We present InterView-C, a German multimodal corpus of 27 survey interviews conducted entirely in virtual reality, with both interlocutors represented by avatars.HySPE: Positional Encoding via Symplectic Dual Shears
We introduce Hyperbolic Symplectic Positional Encoding (HySPE), grounding positional attention in non-compact symplectic transformations.Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents
Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions.GAGR-Lab: Evaluating Joint Spatial-Geometric and Analytic Function Reasoning
Joint spatial-geometric and analytic function reasoning requires translating a perceived spatial configuration into a symbolic function whose executed curve satisfies geometric constraints.From Prompts to Trees: Effective LLM-Guided Tree Generation for Few-Shot Tabular Classification
While Large Language Models (LLMs) possess rich world knowledge and impressive generalization capabilities, their direct application to tabular data classification is hindered by high inference…LLM Persuasion Is in the Eye of the Evaluation
Large language models (LLMs) have already been shown to match or exceed human experts in persuasion.PatchBench: Measuring Collateral Damage in Activation Patching
An LLM safety patch can pass a benchmark while still being a poor repair.SemanticFold: Latent Sequence Compression SeparatesLanguage Modeling, Decodability, and Reasoning
We study whether latent sequence compression of prompt prefixes preserves the capabilities that large language models rely on during inference.Nobody Truly Agrees on Sentiment: Humans, Bespoke Tools, and LLMs Struggle with Social Media Texts
Social media is a rich source of real-time public sentiment, but widely used sentiment analysis tools are often applied without understanding their limitations.Learning to Act with Task Progress: Distilling Small Agents from Compact Teacher Supervision
Learning from large-model demonstrations offers a way to train small agents that can complete recurring tasks without calling a large model at every step.