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Search: “Large Language Models”

16 stories in the last 3d

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Large Language Models Develop Belief State Geometry In-Context

Probing six open-source LLMs on HMM-generated data shows belief states are linearly decodable (R² 0.83–0.99), suggesting in-context learning approximates Bayesian prediction.

Researchers prompted six open-source LLMs with data from 40 hidden Markov models selected for non-trivial belief structure and probed residual-stream activations for belief states (posteriors over hidden states). Belief states were linearly decodable with peak R² values of 0.83–0.99 across HMM/LLM combinations, spanning early to late layers. Patching and steering the probe-identified subspace preserved downstream prediction quality while control interventions degraded performance substantially, establishing functional relevance. The results provide representation-level evidence that in-context learning approximates optimal Bayesian prediction over a context-inferred generative model.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research1

Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation

Study shows LLMs with Mixture-of-Agents and QLoRA finetuning effectively simplify medical texts into plain language while preserving content.

The paper evaluates Plain Language Adaptation (PLA) using GPT-4o-mini, Gemini-1.5-pro, and LLaMA in zero-shot and few-shot settings. It compares prompting strategies, QLoRA finetuning across models, and integrates Mixture-of-Agents (MoA) techniques for robustness. Results demonstrate LLM-driven PLA makes healthcare texts more comprehensible while retaining essential content.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education

Introduces MUSE, a twelve-task benchmark evaluating vision-language models on artistic image understanding in situated educational, Southeast Asian contexts.

MUSE is a benchmark assessing large vision-language models on artistic image understanding across twelve tasks spanning visual perception, semantic and affective interpretation, cultural understanding, and compositional reasoning. It decouples image annotation from question generation for controllable difficulty and curates images centering Singaporean and Southeast Asian multicultural contexts alongside Western art. Evaluations of open-source and proprietary models found substantial disparities, especially in affective interpretation and compositional reasoning.

arXiv cs.AI / cs.LG / cs.CL · 12h agoAI research

Breaking the 1.58-bit Barrier for Ternary LLMs

An arXiv paper claims a method that breaks the 1.58-bit barrier for ternary large language models.

The arXiv preprint 2609.16338, titled 'Breaking the 1.58-bit Barrier for Ternary LLMs,' presents research on ternary-weight large language models, which use roughly 1.58 bits per weight. The source text contained only the title and Hacker News engagement data (56 points, no comments), so further technical details are not available.

Hacker News · AI · 8h agoAI researchHN 56↑ · 0 comments

When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control

Chain-of-Self-Questioning prompting cuts LLM wrong-answer commitments 32% relative while raising answered accuracy, holding across eleven model families.

The paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes LLM answer commitment conditional on an explicit assessment of the information required to answer. On an 817-item TruthfulQA multiple-choice set, Grounded-CoSQ at τ=0.90 reduced mean unconditional wrong-commitment rate from 13.1% under chain-of-thought to 8.9% (a 32.1% relative reduction), while raising answered accuracy from 86.9% to 89.7% at 87.6% coverage. Improvements held across eleven open-weight and hosted model families and at every evaluated threshold, with convergent evidence from a Natural Questions short-answer evaluation.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

Why don't machine learning research agents overfit?

Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.

Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.

Hacker News · security · 2d agoAI researchHN 21↑ · 3 comments

What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity

Pruning study across four LLM architectures finds dense models degrade sharply on smart-home tool calling while MoE models tolerate far more.

Researchers systematically study pruning-induced degradation in smart-home tool calling across four LLMs spanning dense Transformer, dense hybrid, and mixture-of-experts architectures, combining depth, width, hybrid, and expert pruning methods, and evaluate over 19,500 instances from three datasets after post-pruning supervised fine-tuning. Dense models show narrow safe pruning regions followed by sharp degradation, while MoE models tolerate substantially more pruning. Pruning degrades grounded specificity (operation, device, argument, value) before schema-level intent, and aggressive dense pruning can induce systematic over-refusal.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Distress Prediction Across Heterogeneous Surveys

Schema-aware split learning uses LLaMA-3.2-3B-Instruct as shared semantic encoder to harmonize heterogeneous mental-health surveys while raw data stays local.

The paper proposes a schema-aware split learning framework where an LLM serializes heterogeneous mental health survey records into natural language and is fine-tuned via LoRA, partitioned across client and server. Clients keep raw survey responses local and run only a lightweight front-end while the resource-intensive backbone runs server-side. Using LLaMA-3.2-3B-Instruct, the framework attains an average ANLS of 0.708 with 2,000 training samples, beats federated learning in eight of nine settings, and cuts per-client computation by three orders of magnitude while generalizing to unseen datasets.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

A Zeroth-Order Paradigm for LLM Preference Alignment

Researchers propose ComPO, a zeroth-order comparison-based preference alignment method with convergence guarantees that mitigates likelihood displacement in LLMs.

ComPO extracts directional information from preference pairs via comparison oracles instead of optimizing a differentiable preference loss, addressing likelihood displacement in direct alignment methods. The paper establishes convergence guarantees for the offline scheme and introduces an online variant with reverse-KL control using unlabeled policy generations. Experiments on Mistral, Llama, Gemma-2, Gemma-3, and Qwen3 models show improvements over existing direct alignment methods, including length-controlled win rates.

arXiv cs.AI / cs.LG / cs.CL · 11h agoAI research

Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback

CCL couples teacher calibration with student updates via token-level branching, provably removing teacher bias in LLM distillation.

The paper proposes Coupled Calibration and Learning (CCL), an LLM distillation algorithm that alternates teacher calibration using source-question reward feedback with student training on target questions under covariate shift. Each iteration calibrates the teacher on source feedback, trains the student on target questions, and lets the updated student inform subsequent calibration. The authors prove the student's expected KL divergence to the oracle student converges to zero at a polynomial rate, and show regularized direct matching error can remain bounded away from zero.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research1

EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents

EvolveTrade lets LLM trading agents self-refine their tool-use policy from realized portfolio feedback, improving Sharpe ratios.

EvolveTrade treats a tool-using trading agent's system prompt as a text-parameterized policy that a Policy Agent revises after each update interval using accumulated decision traces and realized portfolio feedback, keeping the backbone LLM fixed. Experiments across multiple market regimes and two LLM backbones show improved Sharpe Ratio and Cumulative Return over fixed-policy baselines in most settings. Behavioral analyses show evolved policies increase code-mediated analysis and activate regime-relevant computations, with case-level attributions linking policy changes to returns.

Hugging Face daily papers · 2d agoAI research

Bellman Policy Optimization

Bellman Policy Optimization, a critic-free RLVR method derived from Policy Mirror Descent, improves LLM mathematical reasoning without intermediate state-value estimation.

The paper introduces Bellman Policy Optimization (BPO), a critic-free reinforcement learning method for LLMs with verifiable rewards, derived from Policy Mirror Descent. BPO uses the Bellman equations to reformulate PMD as a trajectory-level objective for autoregressive generation with terminal rewards, avoiding state-value estimation at intermediate states. The authors prove BPO shares the same unique optimal solution as PMD and validate it on mathematical reasoning benchmarks.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering

Evaluation of twelve LLMs on 222 clinical questions shows verbatim quotes rarely substantiate claims; claude-opus-5 fully substantiates only 37.1%.

The authors build a standardized harness over four clinical practice guidelines and evaluate twelve LLMs on 222 synthetic clinical questions, measuring citation attachment, verbatim quote production, and claim substantiation. Most models attach verbatim quotes to over 90% of claims from prompting alone, though lightweight models like claude-haiku-4.5 struggle. Quotes frequently fail to substantiate claims: claude-opus-5 quotes 98.0% of claims but fully substantiates only 37.1%, exposing a capability gap for verifiable clinical QA.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

HypoEvolve couples a generational genetic algorithm with specialized LLM agents to generate drug-repurposing hypotheses, beating six baselines on DepMap selectivity (0.171 vs 0.115).

HypoEvolve coordinates specialized LLM agents through a generational genetic algorithm in which scientific judgments and new proposals reshape a hypothesis population. Evaluation centers on drug repurposing, linking mechanistic explanations to target-level biological claims assessed via external measures adapted from DepMap and Open Targets. Across 34 cancer types, HypoEvolve scores highest against six baselines on both measures, with DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains generalize to held-out cancer types.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research 2 sources1

Higher-order pruning of experts in mixture-of-experts language models

New HOPE method uses second-order objectives to prune Mixture-of-Experts LLMs, outperforming REAP at 50% pruning on models up to 122B parameters.

Researchers derive HOPE (Higher-Order Pruning of Experts), a second-order pruning objective for Mixture-of-Experts language models that provably minimizes an upper bound on pruning error by modeling cooperative expert interactions. They show REAP, a state-of-the-art first-order method, is a special case of HOPE with interaction terms ignored. Across three frontier MoE models up to 122B parameters, two calibration sets, and benchmarks covering math, instruction following, coding, and agentic tasks, HOPE achieves the best average rank (1.58 of 5 at 50% pruning versus 2.42 for REAP), with gains up to +6.1% on agentic coding.

arXiv cs.AI / cs.LG / cs.CL · 12h agoAI research

OPEN-1B: A Fully Auditable Training Run

Open-1B releases a 1B-parameter model with bitwise-reproducible training, letting independent auditors verify every step of the run on commodity hardware.

The paper introduces a 'fully auditable' tier of model transparency: every training operation is reproducible with bitwise certainty on heterogeneous commodity hardware by imposing definite ordering on GPU kernel reductions, data batch ordering, and collective communication. Because replaying a full run on one machine is infeasible, a collective verification scheme lets many independent auditors certify individual steps covering the whole run. The authors release Open-1B with its full pretraining dataset, every intermediate checkpoint, the training codebase, and an audit harness. This rules out undisclosed data, injected biases, or backdoors that proof-of-learning or proof-of-training-data techniques cannot exclude.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research