TOPIC · LLM · 27 PAPERS

LLMs

LLMs Reasoning Alignment Agents Multimodal Visual generation Vision & 3D Robotics & embodied AI Efficient training & inference Safety & interpretability AI for Science Code & SWE Benchmarks & data Reinforcement learning

Last 30 days 27

#05 ▲371 ★115 ❝2 2026-10-13

Jeva: System One Models from First Principles

Jeva is a decision-native System 1 model that scores candidate options directly instead of generating text answers, making it fast and cacheable.

National University of Singapore · Qi Li, Xinchao Wang

LLMs Efficient training & inference arXiv Code HF

#21 ▲240 ★227 ❝0 2026-09-19

OmniEdu: Open Foundation Models for Learning and Teaching

OmniEdu is an open family of K-12 education foundation models covering problem solving, curriculum grounding, diagnosis and tutoring.

Peking University · Hao Liang, Qihan Lin, Meiyi Qiang +5

LLMs arXiv Code HF

#35 ▲273 ★18 ❝0 2026-09-24

Post-Training Leaves Behavioral Shadows on Unrelated Decisions

Shows post-training leaves behavioural shadows in unrelated outputs; a single teacher word per prompt can transfer capabilities to a student.

Peking University · Ziyang Zhang, Yubin Jing, Yuanhao Zeng +3

LLMs Safety & interpretability arXiv Code HF

#38 ▲21 ★2.8k ❝22 2026-09-29

TabFM: A Zero-Shot Foundation Model for Tabular Data

TabFM is a 400M-parameter tabular foundation model trained on synthetic tables that predicts zero-shot in a single forward pass.

Google · Weihao Kong, Erez Louidor Ilan, Shuxin Nie +6

LLMs Benchmarks & data arXiv Code HF

#43 ▲241 ★9 ❝0 2026-10-08

Thinking Inertia: LLMs Keep Thinking When Told Not To

Shows LLMs keep reasoning even when told not to think, and proposes a three-level way to measure no-thinking behaviour.

Stanford University · Dianqiao Lei, Kevin Qinghong Lin, Pan Lu +2

Reasoning LLMs arXiv Code HF

#48 ▲47 ★742 ❝2 2026-09-29

Context Language Models

Context Language Models treat their context as an editable file and decide what to keep, gaining accuracy with fewer FLOPs.

Meta · Rulin Shao, Shannon Zejiang Shen, Junjie Oscar Yin +10

LLMs Agents arXiv Code HF

#60 ▲119 ★272 ❝0 2026-10-19

MemT-1.0: Towards Memory-Native Evolution

MemT-1.0 makes memory intrinsic to the model using Gated DeltaNet-2 and Mixture-of-Storage to scale native memory.

MemTensor · Zhiyu Li, Zeyu Zhang, Linyun Liu +71

LLMs Efficient training & inference arXiv Code HF

#104 ▲80 ★49 ❝0 2026-10-03

ALoDLM: Adaptively Looped Diffusion Language Models

ALoDLM adds token-adaptive latent recurrence to diffusion language models, narrowing the quality gap with autoregressive models.

Amazon · Liancheng Fang, Zhuowei Li, Youngeun Kim +10

LLMs Efficient training & inference arXiv Code HF

#111 ▲92 ❝6 2026-10-08

MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement

Technical report of Xiaomi's omni-modal MiMo-V2.6, focused on scaling RL compute along several dimensions toward self-improvement.

Xiaomi MiMo · Xiaomi LLM-Core Team, :, Zongming Qiao +147

LLMs Reinforcement learning Multimodal arXiv HF

#118 ▲32 ★945 ❝0 2026-10-06

Recurrent Looped Transformer

The Recurrent Looped Transformer lets compute depth grow with sequence length, generalising tasks like parity far beyond training length.

Princeton University · Yifan Zhang, Jichen Feng, Shihan Qin +2

LLMs Reasoning arXiv Code HF

#122 ▲92 ★62 ❝0 2026-10-01

Hierarchical Continuous Diffusion Language Models

Hierarchical continuous diffusion LMs tie the denoised continuous state to valid tokens, improving coherence of parallel decoding.

University of Illinois at Urbana-Champaign · Hui Ren, Zihan Li, Chang Liu +2

LLMs arXiv Code HF

#138 ▲88 ★30 ❝0 2026-10-10

Vela: User Behavior as Supervision Sparks LLM Intelligence

Vela uses real long-term user behaviour records as supervision to improve LLMs at inferring preferences, intent and future actions.

AllSpark Research · Yang Xiao, Yusong Sun, Haoyi Wu +17

LLMs arXiv Code HF

#144 ▲33 ★141 ❝4 2026-09-21

From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health

A survey of LLMs in mental health, tracing the evolution from pattern recognisers to personalised companions.

Affiliation unverified · He Hu, Yucheng Zhou, Qianning Wang +8

LLMs AI for Science arXiv Code HF

#201 ▲35 ★49 ❝3 2026-10-05

Towards Looped Models Done Right, Part II: Rethinking at Fixed Points

Driving looped language models towards fixed points enables truncated backprop, KV sharing and cheaper RL with little accuracy loss.

Institute of Foundation Models · Benhao Huang, Chufan Shi, Junlin Chen +4

Efficient training & inference LLMs arXiv Code HF

#214 ▲148 ❝0 2026-10-20

PAR: Progress-Aligned Recurrence for Length Generalization in State Tracking

PAR aligns each loop iteration of a looped LM with task progress and anchors state norms, extrapolating state tracking to 16x training length.

ant-international · Pan Wang, Yihao Hu, Guangyuan Dong +8

LLMs Reasoning arXiv HF

#222 ▲101 ❝0 2026-09-29

Follow the Entities: A Corpus Map for Agentic Search

Microsoft builds an entity-based corpus map so search agents can follow evidence across documents instead of rediscovering links.

Microsoft · Soyeong Jeong, Sujay Kumar Jauhar, Sung Ju Hwang +1

Agents LLMs arXiv HF

#230 ▲50 ★37 ❝0 2026-10-09

Multi-Stream Attention Residuals

Multi-Stream Attention Residuals extend depth-wise attention residuals to multiple heads, with sizeable gains at little extra cost.

Shanghai Jiao Tong University · Xiaohan Qin, Xiangdong Zhang, Tuo Dai +7

LLMs Efficient training & inference arXiv Code HF

#235 ▲91 ❝0 2026-10-06

TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models

Qwen's TRACE aligns the training and rollout FP4 quantisation paths for low-precision RL of MoE language models.

Qwen · Xin Wang, Hao Yu, Zhengyang Zhuge +9

Efficient training & inference Reinforcement learning LLMs arXiv HF

#236 ▲27 ★187 ❝1 2026-10-06

On KL-Regularized Policy Optimization

KLPO anchors the KL regulariser at the sampling policy to handle stale, mismatched rollouts in asynchronous LLM-agent RL.

math-ai · Yifan Zhang

Reinforcement learning LLMs arXiv Code HF

#240 ▲41 ★14 ❝0 2026-10-15

AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning

Tsinghua's AutoSFT gives an agent a full data action space—mix, prune, repair, synthesise—to automate SFT data research.

Tsinghua NLP Group · Bingxiang He, Pengqi Sun, Shihao Peng +18

Agents LLMs arXiv Code HF

#255 ▲68 ★10 ❝0 2026-09-29

E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models

E-MoE improves non-factorised diffusion language models with enhanced mixture-of-experts, raising few-step quality without posterior collapse.

Affiliation unverified · Arseny Ivanov, Alexander Kolesov, Alexander Korotin +2

LLMs Efficient training & inference arXiv Code HF

#259 ▲79 ★3 ❝0 2026-10-13

Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders

ReMaRk scores recommendation lists against the union of all replies, removing responder noise from GRPO training of LLM recommenders.

Nanyang Technological University · Xin Li, Hao Jiang, Jinghao Guo +2

Reinforcement learning LLMs arXiv Code HF

#266 ▲54 ★11 ❝0 2026-10-06

U-Space: Uncovering When and Why Uncertainty Arises in Language Models

U-Space analyses when and why uncertainty arises in language models, to help judge whether an individual answer can be trusted.

Multimodal AI Lab - Technical University of Darmstadt · Tobias Braun, Nils Loose, Alexander Herzog +4

Safety & interpretability LLMs arXiv Code HF

#269 ▲73 ❝0 2026-10-14

Cortex: Consolidating Context into Parametric Memory

Cortex consolidates gains from context into parametric memory, keeping them after the context is removed.

ByteDance Seed · Jintian Zhang, Feng He, Xinnian Liang +5

LLMs Efficient training & inference arXiv HF

#271 ▲68 ★5 ❝0 2026-10-18

CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?

CoEvo-RSI tackles the brain–body mismatch when models and agent harnesses co-evolve, using a meta-agent to coordinate harness evolution and checkpoint choice.

Affiliation unverified · Jiaqi Su, Guibin Zhang, Jie-Jing Shao +5

Agents LLMs arXiv Code HF

#278 ▲55 ★6 ❝0 2026-10-15

Lexical Reference Frames: Interpreting LM Residual Streams in Native Semantics

Lexical reference frames read hidden states through the model's own remaining layers, giving far more faithful readings than fixed lenses.

Affiliation unverified · Jian Gu, Hongyu Zhang, Chunyang Chen +1

Safety & interpretability LLMs arXiv Code HF

#304 ▲28 2026-10-19

Kronecker Delta Rule for Long Context Linear Attention

KronLSTM introduces a Kronecker delta rule that revises matrix memory along one direction, giving stable long-context linear attention that often beats DeltaNet.

NX-AI · Anamaria-Roberta Hartl, Levente Zólyomi, Michael List +3

Efficient training & inference LLMs arXiv HF

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