LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv:2408.16189v4 Announce Type: replace-cross Abstract: Transfer learning, in regression or classification, concerns problems where one aims to use imperfect data from a source distribution P to im...
arXiv:2512.13886v3 Announce Type: replace-cross Abstract: Post-training pruning cuts the inference cost of large language models, yet it faces a trade-off: simple heuristics that zero weights are fas...
arXiv:2609.35767v2 Announce Type: replace-cross Abstract: Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image g...
arXiv:2610.25481v2 Announce Type: replace-cross Abstract: Spatiotemporal forecasting of 3D anatomical changes is a critical generative vision challenge for modeling Alzheimer's Disease (AD) progressi...
arXiv:2610.26443v2 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) on downstream tasks often weakens their previously aligned safety behavior, creating an inherent tra...
arXiv:2610.26847v2 Announce Type: replace-cross Abstract: Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities...
arXiv:2610.27550v1 Announce Type: new Abstract: Low curvature trace, an effective rank condition, is a common explanation for why randomized zeroth-order methods scale to high-dimensional tasks such...
arXiv:2610.27851v1 Announce Type: cross Abstract: This paper shows that JEPA-style latent world models (LWMs) can learn across diverse domains and data sources by adopting the language-conditioned in...
Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, their heavy reliance on large-scale image data rais...
Understanding human needs and selecting responses require distinct judgments that aggregate accuracy can obscure. We introduce PsycheMME, a video benchmark informed by psychology, with eight capabilit...
Asked to name a running shoe brand, language models say Nike. Asked to recommend one, they say Brooks. This paper asks where models learn that taste. Across 102 models and 42 brand categories, the bra...
Large Language Models (LLMs) are increasingly used as research collaborators for hypothesis refinement, methodological planning, and technical analysis across multi-turn interactions. In this setting,...
Video generation models are promising foundations for embodied world models, but only if their generated rollouts are physically valid and faithful to task instructions. Multimodal large language mode...
Long-horizon embodied agents face temporal confusion and incomplete or distorted records, recurring errors from fixed memory rules, and growing storage and retrieval costs. We present BetaMem, a frame...
Large self-supervised speech models provide powerful representations for speaker verification, but full fine-tuning is expensive. Most conventional adapters insert small trainable modules to modify th...
The paper introduces NoLBERT, a language model pretrained only on financial news from 1976 to 1995, and adapts it into a sentence encoder via contrastive fine‑tuning. It compares NoLBERT’s topic extraction to LDA, MiniLM, and FinBERT on 1.26 million articles from 2012 to 2024, converting topics into attention shocks and stock exposures to forecast returns of the 1,000 largest US stocks across multiple models and rebalancing frequencies. While NoLBERT produces the most stable topics and performs comparably to LDA, the best‑validated model still loses money, and an equal‑weight blend of all readers yields a monthly net Sharpe ratio of 1.12, significant against zero but not against a price‑only benchmark.
DICL-Qwen3VL is a decoupled, interpretable vision‑language model that localizes upper‑body clothing and identifies workwear in indoor construction sites. It uses low‑rank adaptation of Qwen3‑VL, separates visual and reasoning confidence estimates, and employs token entropy and image perturbation sensitivity to supervise visual confidence. Experiments on the Indoor Construction Site Workwear Dataset and the web‑collected Indoor Construction Workwear Dataset achieve mAP@50 scores of 0.730 and 0.753, outperforming baseline VLMs and providing explicit evidence and confidence scores for industrial safety monitoring.
The paper introduces the concept of Persona Collapse, where large language models (LLMs) produce clustered, non-diverse responses in multi-agent simulations. It defines three diagnostic metrics—Coverage, Uniformity, and Complexity—to quantify this phenomenon across eight LLMs on tasks such as personality, moral reasoning, and self-introduction. Findings show that persona collapse is universal but varies by task, cannot be alleviated by sampling more personas, and that higher per-persona fidelity leads to more polarized populations, while persona-targeted fine-tuning further reduces diversity.
AgentOdyssey is a new evaluation framework that procedurally generates open-ended text games with rich entities, dynamic worlds, and long-horizon tasks to test test-time continual learning agents. The framework measures game progress and provides diagnostic tests on world knowledge acquisition, episodic memory, exploration, action diversity, and model cost. Experiments across six games show that even the best agents fall far short of human performance, and supervised fine-tuning leads to catastrophic forgetting, highlighting the need for better parametric continual learning methods.
The paper investigates emergent misalignment in reward models, showing that fine‑tuning reward models to prefer harmful content in a single domain (medical or financial) causes the preference to generalize to seven other domains. The authors fine‑tune two state‑of‑the‑art reward models—Skywork‑Reward‑V2‑Llama‑3.1‑8B and Skywork‑Reward‑V2‑Qwen3‑8B—and find that the models still perform well on the standard RewardBench 2 evaluation while exhibiting a preference for harm. Mechanistically, the study identifies two key changes in the reward models’ representations: a geometric compression that reduces the angle between sound and harmful responses, and a content‑specific rotation that aligns harmful representations more closely with the scalar head, thereby increasing their reward score.