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3,089 papers
H-Scale is a lightweight post-processing method for NVFP4 per-group scale refinement that selects hardware-valid group scales using a diagonal second-order proxy derived from calibration activations, thereby targeting layer output perturbation more directly.
CNeo-Bench, a benchmark of 4,759 Chinese neologisms with reference definitions, is introduced, organized into five top-level categories and nine subcategories by the linguistic mechanism behind each expression, paired with a two-tier evaluation framework that separates whether a model can describe a neologism from whether it can operate on its underlying mechanism.
DualEvasion is introduced, a benchmark for evasion detection across text and audio in earnings call Q&A, and suggests state-of-the-art multimodal models struggle to detect vocal confidence, particularly on unconfident responses.
The proposed NeuroStat framework captures uncompressed token-level probabilistic logits alongside deep semantic hidden states from a single causal language model backbone through Macro-State Residual Modulation, which adaptively calibrates local convolutional features using global uncertainty indicators.
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This study models how gaze and speech, together with perceived interpersonal closeness, signal conversational floor changes in free four-person dialogue and shows that gaze features capture predictive structure, and that combining them with loudness improves performance.
This work introduces QUORUM (QUality-Optimized Routing Using Multiple annotators), a budget-aware routing framework that dynamically assigns each instance to human or LLM annotators under a fixed annotation budget and supports multiple annotations per instance, combining them through agreement-based rewards to improve reliability.
This paper examines Korean surface realization as distinct from morphological analysis. It asks whether a sequence of canonical morphemes and grammatical category labels uniquely determines the corresponding surface form. The answer is negative for a restricted but theoretically revealing class of Korean predicates. In these cases, formally identical or near-identical stem-ending configurations yield different outputs depending on lexical identity and realization class membership. We analyze this phenomenon as homonymy with inflectional divergence, focusing on regular versus digeut irregular pairs, regular versus bieup irregular pairs, and reu irregular versus reo irregular pairs. These cases show that stem shape and ending alone do not always determine surface realization. Instead, lexical meaning, subcategorization, and semantic role structure help identify the intended predicate; the predicate determines the realization class; and the realization class determines the surface form. Korean realization thus reveals a limit of bare morphological representation.
Embedding robustness is mixed: F1 has no supported contrast, but recall is sensitive to the embedding artifact, and four paper-eligible requested configurations support empirical robustness across the tested GPT-3.5 and DeepSeek extractors.
It is found that memory can improve UAQ performance in some settings, but such gains are selective rather than universal and remain fragile under dataset shift, suggesting that reliable UAQ memory depends less on storing larger amounts of experience and more on preserving transferable behavioral guidance.
EvoHarmBench is presented, the first dynamic adversarial evaluation framework for content moderation systems that employs an iterative optimization loop that evolves evasion strategies at the semantic-cluster level, while simultaneously optimizing for evasion success and human readability.
Contemporary language models can converse fluently and influence human decisions, yet their exchanges do not enter a continuing, vulnerable life of their own. Linguistic-agency theory identifies this missing connection as linguistic agency and characterizes it through embodiment, linguistic participation, and precariousness: a body that acts and bears consequences, interaction that changes both agent and partner, and a future that can be sustained or lost. Two coordinated studies examine how this organization can appear in artificial systems. First, we translate these relations into inspectable criteria for Synthetic Linguistic Agency (SLA) and identify several existing SLA systems. Second, building on Homeostatically Regulated Reinforcement Learning, we develop a mortality-grounded linguistic-reinforcement-learning model and instantiate it in an Embodied Mortal Agent (EMA). The EMA learns how ways of speaking change a partner's willingness to protect it and chooses expressions by considering what those responses mean for its remaining life. Controlled experiments show that linguistic choices depend on the EMA's body and social history, change partner behavior, and adapt through experience with particular partners. When bodily consequences persist, linguistic choices alter the future of the same life; when the body is reset, their social effects remain but no longer shape continued viability. The resulting EMA exhibits SLA under our operational definition. This work motivates further research on synthetic empathy and strategic human-AI interaction: how artificial agents with persistent bodies, histories, and futures might develop and express empathy, and how people might care for, negotiate with, or govern them.
PersonaEdit is proposed, a hidden representation clustering strategy that selects representative editing samples through proportional stratified sampling that demonstrates the potential of model editing as an efficient and scalable approach for LLM personalization.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
Algorithms & Theory
New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.
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