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🔬 RESEARCH
via Arxiv
👤 Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang et al.
📅 2026-06-25
⚡ Score: 7.4
"Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth answers to assign rewards, limiting their applicability to tasks where the ground-truth solution is unknown. We introduce a \textbf{R}anking-\textbf{i}nduced \textbf{VER}ifiable framework (RiVER) t..."
🔬 RESEARCH
via Arxiv
👤 Bo Shen, Lifeng Chang, Tianyuan Wei et al.
📅 2026-06-26
⚡ Score: 7.3
"The transition from static chat bots to autonomous agents--equipped with persistent memory, tool-use protocols, and multi-agent collaboration--has fundamentally expanded the AI threat landscape. Current defense mechanisms, such as perimeter security and training-time alignment, remain external to th..."
🔬 RESEARCH
"Multi-model LLM systems such as routing, voting, cascades, fusion, and mixture-of-agents are used to beat single-model accuracy. We show that their gain is capped by a quantity the field rarely reports. For any policy whose output is one member model answer, accuracy cannot exceed one minus beta, wh..."
🔬 RESEARCH
via Arxiv
👤 Hamid Reza Firoozfar, Mohammadsadegh Abolhasani, Reza Mousavi et al.
📅 2026-06-25
⚡ Score: 7.2
"To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive meanings. Such expressions surface as algospeak, euphemisms, and adversarial obfuscation, depending on intent and context, and they involve recurring enco..."
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🔬 RESEARCH
via Arxiv
👤 Preet Baxi, Jiannan Xu, Jane Yi Jiang et al.
📅 2026-06-25
⚡ Score: 6.9
"Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate algorithmic hiring systems. We study prompt injection in automated résumé screening, defined as subtle self-promotional text that introduces no new qua..."
🔬 RESEARCH
via Arxiv
👤 Ruixuan Huang, Yipei Wang, Wenyi Fang et al.
📅 2026-06-26
⚡ Score: 6.8
"Frontier large language model training consumes massive accelerator fleets and long wall-clock computation, making stability failures costly when they occur. After a numerical or a hyperparameter fault has already destabilized the training dynamics, it may continue for thousands of steps while loss..."
🔬 RESEARCH
"Autonomous coding agents now open and merge pull requests in shared repositories at scale, and the field evaluates them the way it has always evaluated components, one agent at a time, on isolated benchmark tasks. Yet agents that each pass their own tests still leave repositories that accumulate pro..."
🔬 RESEARCH
"The AI community has framed the relationship between large language models (LLMs) and world models as a dichotomy: LLMs predict tokens; world models simulate reality. Yann LeCun argues in 2022 that reaching general intelligence requires abandoning autoregressive token prediction in favour of latent-..."
🔬 RESEARCH
"Recurrent models must forget in order to remember, yet the state of the art decides what to erase without consulting what is stored -- the gate sees only the arriving token, not the memory it is about to modify. This memory-blind gating is one of three coupled defects in the leading delta-rule archi..."
🔬 RESEARCH
via Arxiv
👤 Tianyi Men, Zhuoran Jin, Pengfei Cao et al.
📅 2026-06-25
⚡ Score: 6.5
"Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions. While small open source MLLMs are cost efficient and privacy preserving compared with commercial large models, they suffer from..."
🔬 RESEARCH
via Arxiv
👤 Nicklas Hansen, Xiaolong Wang
📅 2026-06-25
⚡ Score: 6.4
"Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics. We hypothesize that hallucination concentrates in low-coverage regions of the state-action space,..."
🔬 RESEARCH
via Arxiv
👤 Nathanaël Jacquier, Maria Vakalopoulou, Mahdi S. Hosseini
📅 2026-06-25
⚡ Score: 6.3
"Sparse autoencoders (SAEs) have become a leading tool for interpreting the representations of vision foundation models, decomposing their polysemantic activations into a larger set of sparse, more monosemantic features. The Top-$k$ SAE, a now-standard variant, enforces sparsity architecturally throu..."
🔬 RESEARCH
via Arxiv
👤 Junhao Shi, Zezheng Huai, Siyin Wang et al.
📅 2026-06-25
⚡ Score: 6.3
"Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT) and physical (manipulation, navigation) domains, coupled with autonomous recovery from physical failures that inevitably arise over extended operation..."
🔬 RESEARCH
via Arxiv
👤 Sangwoo Cho, Kushal Chawla, Pengshan Cai et al.
📅 2026-06-25
⚡ Score: 6.1
"Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug. We propose BINEVAL, a framework that decompose..."