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Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv AI
Oct 2

ReHoPER: Receding-Horizon Planning for Enhanced Reasoning

ReHoPER is an inference‑only, zero‑shot method that enhances large language models’ reasoning by generating and answering intermediate questions along multiple paths before producing a final answer. It plans a horizon of candidate intermediate questions, selects one to answer, and replans based on the updated history. The approach is task‑agnostic, using generic instructions across datasets and models without labeled data or task‑specific prompt design, and it outperforms strong baselines on several datasets, notably achieving the largest gains on the new iLLC benchmark for compositional reasoning.

By Saeed Ahmadnia, Cornelia Caragea
llmsbenchmarks
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arXiv AI
Oct 2

Distilling Directional Verification

arXiv:2610.00997v1 Announce Type: cross Abstract: Knowledge distillation aims to transfer the factual knowledge of large language models to smaller models for efficient deployment. Yet a teacher may...

By Jungseob Lee, Sugyeong Eo, Seongtae Hong, Seungyoon Lee, Chanjun Park, Jaehyung Seo, Heuiseok Lim
llmsefficiency
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arXiv AI
Oct 2

Sharpening Tax in Post-Training

The paper investigates how reinforcement learning post‑training of large language models (LLMs) tends to sharpen existing behaviors, improving single‑shot accuracy but reducing solution coverage. It shows that pre‑trained LLMs, when paired with a lightweight inference harness, can outperform post‑trained models in coverage for agentic tasks that require multi‑turn tool use. The authors introduce the Sharpening Tax metric to quantify this trade‑off, analyze its prevalence across 14 model pairs and 42 benchmark cases, and propose a Bayesian sampler, PTGS, that mitigates the tax by adapting sampling temperature to prompt difficulty.

By Changdae Oh, Qi Zeng, Qi Qi, Andrey Zhmoginov, Deren Lei, Yun He, Hoang Phan, Hangoo Kang, Azalia Mirhoseini, Sharon Li
llmsagentsreinforcement-learningbenchmarks
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arXiv AI
Oct 2

Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor's Internal States

The paper introduces POISE, a reinforcement learning algorithm that uses a model’s internal states as a value estimator to reduce variance in reinforcement learning with verifiable rewards (RLVR). By employing a lightweight probe that reads internal signals during the forward pass, POISE predicts baselines online and uses a cross‑rollout construction to keep gradients unbiased. Experiments on Qwen3‑4B and OLMo3‑7B‑Instruct‑DPO across six domains show POISE outperforms existing RLVR baselines, offering more stable training and a value model that generalizes across tasks and scales with the policy.

By Yunho Choi, Jongwon Lim, Woojin Ahn, Minjae Oh, Jeonghoon Shim, Yohan Jo
llmsreinforcement-learning
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arXiv AI
Oct 2

Reshape and Recur: Improving SSMs with Input Reshaping and Depth Recurrence

The paper proposes two extensions to State Space Models (SSMs) to reduce memory usage and improve performance. First, it introduces depth recurrence, allowing a looped SSM with fewer parameters to match the performance of a larger, non-recurrent model. Second, it advocates using a fixed time granularity across tasks by reshaping input sequences, which enhances how information is presented to the model. Both techniques consistently benefit four representative SSM architectures (LRU, S5, LinOSS, LrcSSM).

By M\'onika Farsang, Ramin Hasani, Daniela Rus, Radu Grosu
llmsragefficiency
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arXiv AI
Oct 2

UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models

UniGuardian is a training‑free detector for large language models that jointly identifies prompt injection, backdoor, and adversarial attacks—collectively called Prompt Trigger Attacks (PTA). It measures how structured prompt perturbations shift the model’s output distribution and uses a single‑forward strategy to detect attacks while generating text in a shared batched forward pass. Experiments show that UniGuardian accurately and efficiently identifies trigger‑activated prompts in LLMs.

By Huawei Lin, Yingjie Lao, Tony Geng, Tan Yu, Weijie Zhao
llmssafety
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