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🔬 RESEARCH
"As multi-agent, tool-using LLM systems are deployed, a common safety net is a runtime monitor that checks each message, tool call, or step on its own. We show this net has a fundamental hole. A distributed backdoor splits a harmful payload across agents, so every local check passes while the assembl..."
🔬 RESEARCH
via Arxiv
👤 Roi Cohen, Yvan Carré, Nick Lechtenbörger et al.
📅 2026-07-14
⚡ Score: 7.1
"Language models encode substantial factual knowledge in their parameters, which can lead to unreliable behavior when this knowledge is outdated, incomplete, or misaligned with the provided context. In this work, we study whether modifying the pretraining signal can systematically shift models away f..."
🔬 RESEARCH
via Arxiv
👤 Sen Yang, Yuen-Hei Yeung
📅 2026-07-14
⚡ Score: 7.1
"Aligned language models routinely misreport under non-evidential incentive pressure: they agree with a confident user or overstate certainty even when their internal belief is unchanged. We cast this as a failure of internal incentive-compatibility (IC) and present a method for learning and certifyi..."
🔬 RESEARCH
via Arxiv
👤 Xing Zhang, Guanghui Wang, Yanwei Cui et al.
📅 2026-07-14
⚡ Score: 7.0
"Self-evolving agent systems improve by creating, revising, and retiring their own skills, but every such loop rests on a hidden assumption: a reliable evaluation metric already exists. In many real applications it does not. We make three claims. First, metrics can be \emph{evolved}: our metric loop..."
🔬 RESEARCH
via Arxiv
👤 Chalamalasetti Kranti, Sowmya Vajjala
📅 2026-07-14
⚡ Score: 7.0
"LLM judges are increasingly being used to evaluate open-ended model responses, often in no-reference settings where a ground-truth answer is unavailable. However, can they reliably assess in such evaluation setups? We explore this question in this paper through a two stage pipeline with a) calibrati..."
🔬 RESEARCH
"Transformer language models are increasingly used as software components, yet biased outputs remain difficult to localize and repair inside the model. Existing fairness testing and repair methods largely operate at the input-output or retraining level, while recent work suggests that bias-related be..."
🔬 RESEARCH
via Arxiv
👤 Samuel Yeh, Yiwen Zhu, Shaleen Deep et al.
📅 2026-07-14
⚡ Score: 6.9
"Failure attribution for LLM-based agentic systems, i.e., identifying which steps in a failure trajectory caused the task to fail, is critical for debugging and improving these systems. Existing approaches either rely on prompting-based pipelines, which are computationally expensive, or require post-..."
🔬 RESEARCH
via Arxiv
👤 Hanhua Hong, Yizhi Li, Jiaoyan Chen et al.
📅 2026-07-14
⚡ Score: 6.9
"Rubric-based evaluation is a promising approach for assessing open-ended outputs from LLM-based research agents, particularly in paper reproduction, where direct paper-to-repository comparison is prone to hallucination. However, constructing paper-specific rubrics requires substantial expert effort,..."
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🔬 RESEARCH
via Arxiv
👤 Yanzhe Zhang, Sanmi Koyejo, Diyi Yang
📅 2026-07-14
⚡ Score: 6.9
"As large language models (LLMs) grow more capable, they are increasingly deployed in context-rich settings where task inputs are often accompanied by long, partially irrelevant context. In a controlled setting, we find that state-of-the-art models often appear robust to task-irrelevant context at th..."
🔬 RESEARCH
"Plan evaluators can reward a strategic plan for becoming less explicit. This paper studies that failure in a staged expected-value scorer for LLM-generated venture routes. Proposition 1 gives the score change from deleting an interior transition while retargeting its predecessor and retaining downst..."
🔬 RESEARCH
via Arxiv
👤 Daehoon Gwak, Minhyung Lee, Junwoo Park et al.
📅 2026-07-14
⚡ Score: 6.8
"Diffusion large language models (dLLMs) offer a theoretical advantage in parallel generation over standard autoregressive models. However, parallel generation alone does not guarantee practical speedups. Realizing this efficiency requires specialized inference mechanisms, such as diffusion-aware cac..."
🔬 RESEARCH
via Arxiv
👤 Xixuan Hao, Zeyu Zhang, Zehao Lin et al.
📅 2026-07-14
⚡ Score: 6.8
"Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions. Existing benchmarks, however, evaluate such memory almost exclusively through downstream question answering, scoring only the correctness of a final answer. Th..."
🔬 RESEARCH
via Arxiv
👤 Junjie Yin, Xinyu Feng
📅 2026-07-14
⚡ Score: 6.8
"Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edi..."
🔬 RESEARCH
via Arxiv
👤 Xiaoyu Li, Zheng Gao, Xiaoyan Feng et al.
📅 2026-07-14
⚡ Score: 6.8
"A watermark in a generative model's output is usually asked only whether a text is machine-made. The same mark can do more: attribute it to the user who produced it, extract a hidden payload, or localize the part that survives editing. These form a forensic ladder, and we ask what each rung costs in..."
🔬 RESEARCH
via Arxiv
👤 Shikai Qiu, Marc Finzi, Yujia Zheng et al.
📅 2026-07-13
⚡ Score: 6.8
"Compression is fundamental to intelligence. A model that can represent its training data as a short code has discovered regularities that enable generalization. Large neural networks may learn functions far simpler than their parameter counts suggest, but it is challenging to construct codes that re..."
🔬 RESEARCH
via Arxiv
👤 Monica Munnangi, Saiph Savage
📅 2026-07-14
⚡ Score: 6.7
"Patients seeking medical information often ask questions that embed incorrect assumptions or misconceptions. In such cases, safe medical communication requires not only answering the question, but identifying and correcting the underlying false belief. These interactions naturally unfold over multip..."
🔬 RESEARCH
"Selective state-space models such as Mamba route information through a bank of first-order modes whose input coupling is set by a learned selection mechanism. We give an exact instrument for measuring how a trained model uses these modes. Because the state matrix is diagonal, each channel's output d..."
🔬 RESEARCH
via Arxiv
👤 Zixiang Xu, Sixian Li, Huaxing Liu et al.
📅 2026-07-13
⚡ Score: 6.6
"Existing studies of LLM-as-judge scoring bias work predominantly at the input-output level: they perturb inputs, measure score deltas, and propose prompt-level mitigations. We argue that the same biases admit a representation-level account in the judge's hidden state, complementary to the input-outp..."
⚖️ ETHICS
🎯 Agency vs. Automation • Critical Thinking Required • Tool-Assisted Mastery
💬 "I'm not replacing learning, thinking, or deciding. I think this is the key difference."
• "If you use an LLM to do most of your thinking, what's left?"
🗞️ THE WEEK, EDITED
OpenAI shipped GPT-5.6 with desktop agents, Meta undercut everyone on API pricing, Nvidia pushed its next-gen rack to 2028, and the industry quietly discovered that its newest models are worse at the tasks they were supposedly built for.
🗄️ FROM THE ARCHIVE
Recent daily Metamesh snapshots with preserved AI news rankings, clusters, source links,
and ticker commentary.