Large language model (LLM) assistants increasingly advise backers on which crowdfunding campaigns to support. LLMs have known race and gender problems, and it is unsettled whether they reproduce, erase, or reverse the discrimination documented in entrepreneurial finance. We ask when, and through which decision process,...
Mirza Samad Ahmed Baig, Syeda Anshrah Gillani· 0 citations
Long-context inference is bound by the KV cache, which 2-bit codes could shrink about sixfold if their error stays in check. The usual remedy rotates keys before quantizing, assuming rotation always helps. However, we observe that it does not: rotation flattens a spiky piece of a key but can give a flat one a new peak,...
Kunming Shao, Xijie Huang, Jierun Chen et al.· 0 citations
Enterprises increasingly want LLM agents to automate standard operating procedures over their software stacks -- mapping each step of a procedure to the right call among thousands of internal APIs, without hand-authored step-to-API mappings. The core difficulty is API selection at scale: choosing and composing the corr...
Rongzhe Wei, Ge Shi, Min Cheng et al.· 0 citations
Assistive systems typically tell users everything, often spending time and attention on what a user already knows, leaving less for what happens next. People, on the other hand, leave out what they expect the listener can supply. Can people use their prior knowledge to understand an incomplete message when the speaker...
Anwesha Das, Vera Demberg, A. Feit· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Small language models (<=9B parameters) can support local medical question answering (QA) while reducing privacy and latency concerns, but effective fine-tuning remains challenging. This paper compares two chain-of-thought (CoT) supervised fine-tuning methods: Uniform-across-Sequence (UaS), which applies equal loss wei...
Le Zhou, Xiao Luo, Chinmay Chandra et al.· 0 citations
This work investigates whether open-weight models can generate less verbose reasoning by changing the form of their own earlier thoughts, without retraining, and identifies reasoning history as one way to reduce future reasoning-token use and motivate model-specific compaction policies evaluated for both efficiency and...
A. Hristov· Proceedings of the Second Wo...· 0 citations
LONGINUS is proposed, a retrieval-memory multi-agent adjudication framework combining evidence retrieved from patient records with structured memory of the patient trajectory that achieves the best transition-specific performance among LLM-based methods in nearly all settings while remaining competitive with them on ag...
Inductively Gated Cross-Attention (IGCA), a zero-initialized cross-attention bridge that preserves pretrained model behavior at initialization while enabling gradual multimodal conditioning, is introduced.
Do research agents have taste, the judgment of which experiments are worth running before their outcomes are known? We give six frontier models seven recent NLP papers. Each model, working as a research agent alone or as a team, proposes minimum viable experiments (MVEs), forecasts whether each will pass a threshold it...
Linear activation steering is a powerful approach for controlling and specializing large language models by adding linear representations of concepts to their activations at inference. While effective, existing methods often steer with a fixed strength, independent of how much steering any given input requires. In this...
Brandon Hsu, Daniel Beaglehole, Adityanarayanan Radhakrishnan et al.· 0 citations
Post-training updates change what large language models (LLMs) can answer, but may leave their answerability judgments misaligned. We study this knowledge-judgment desynchronization through functional feeling of knowing (FoK), which supports selective generation, retrieval triggering, and model routing without implying...
Zichuan Fu, Xian Wu, Jingtong Gao et al.· 0 citations
Large reasoning models achieve strong performance on challenging tasks, but their long chain-of-thought often incurs substantial inference costs by continuing to deliberate after sufficient information has been obtained. Existing reasoning compression methods primarily operate on reasoning trajectories, paying less att...
Mu-Yao Yuan, Li-Yan Xu, Wei-Zhan Zhang et al.· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
