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π° NEWS
πΊ 75 pts
β‘ Score: 8.0
π° NEWS
πΊ 255 pts
β‘ Score: 8.0
π° NEWS
πΊ 146 pts
β‘ Score: 7.5
π° NEWS
πΊ 1 pts
β‘ Score: 7.4
π¬ 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..."
π° NEWS
πΊ 1 pts
β‘ Score: 7.3
π¬ RESEARCH
via Arxiv
π€ Yupu Hao, Zhuoran Jin, Huanxuan Liao et al.
π
2026-06-24
β‘ Score: 7.3
"Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancing model capabilities. However, RL alone often leads to instability or limited gains in tool-use tasks. In our experiments, some models exhibit catas..."
π¬ RESEARCH
via Arxiv
π€ Juliana Li, Diya Sreedhar
π
2026-06-24
β‘ Score: 7.3
"Midway through an ordinary pretraining run, a small language model learns the pronoun-gender rule: cued with a girl's name ("Sue cried because"), it resolves the next pronoun to she, generalizing to held-out probes (0.94 by step 925). By step 3,500 the same model scores near zero on the same probes,..."
π¬ RESEARCH
via Arxiv
π€ Martijn Bartelds, Federico Bianchi, James Zou
π
2026-06-24
β‘ Score: 7.3
"Speech conveys information through both words and vocal delivery. We evaluate four leading production realtime voice systems-OpenAI's GPT Realtime 2, Google's Gemini 3.1 Flash Live, and Alibaba's Qwen3.5 Omni Plus and Omni Flash-on tasks where the words and the delivery patterns both convey meaningf..."
π¬ RESEARCH
via Arxiv
π€ Seth Dobrin, Εukasz Chmiel
π
2026-06-24
β‘ Score: 7.3
"AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems. The dominant approach places controls inside the agent's own runtime: system prompts, output filters, and guardrail libraries. Any control in the agent's address space is reachable..."
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π¬ RESEARCH
via Arxiv
π€ Aditya Singh, Gerson Kroiz, Senthooran Rajamanoharan et al.
π
2026-06-24
β‘ Score: 7.3
"A central goal of safety research is determining whether a model is misaligned. Prior work has largely focused on detecting concerning behavior. But behavior alone does not establish misalignment: a concerning action can arise from benign causes such as confusion. This motivates model forensics: inv..."
π° NEWS
πΊ 1 pts
β‘ Score: 7.2
π° NEWS
πΊ 2 pts
β‘ Score: 7.2
π° NEWS
πΊ 1 pts
β‘ Score: 7.1
π° NEWS
πΊ 1 pts
β‘ Score: 7.0
π° NEWS
πΊ 2 pts
β‘ Score: 7.0
π° NEWS
πΊ 2 pts
β‘ Score: 7.0
π° NEWS
πΊ 1 pts
β‘ Score: 6.9
π¬ RESEARCH
via Arxiv
π€ Preet Baxi, Jiannan Xu, Jane Yi Jiang et al.
π
2026-06-25
β‘ 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 rΓ©sumΓ© screening, defined as subtle self-promotional text that introduces no new qua..."
π οΈ SHOW HN
πΊ 2 pts
β‘ Score: 6.9
π° NEWS
πΊ 3 pts
β‘ Score: 6.8
π° NEWS
πΊ 1 pts
β‘ Score: 6.8
π¬ RESEARCH
via Arxiv
π€ Junhao Shi, Zezheng Huai, Siyin Wang et al.
π
2026-06-25
β‘ Score: 6.8
"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..."
π° NEWS
πΊ 1 pts
β‘ Score: 6.7
π οΈ SHOW HN
πΊ 1 pts
β‘ Score: 6.7
π¬ RESEARCH
via Arxiv
π€ Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang et al.
π
2026-06-25
β‘ Score: 6.7
"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..."
π¬ RESEARCH
via Arxiv
π€ TΓ’nia Carvalho, Maxime Cordy
π
2026-06-24
β‘ Score: 6.7
"Tabular foundation models are commonly assumed to present limited privacy concerns as they are often pre-trained on large collections of synthetic data. However, these models leverage in-context learning, where sensitive records may be provided directly at inference time as labelled context examples..."
π¬ RESEARCH
via Arxiv
π€ Wen Ye, Peiyan Li, Tingyu Yuan et al.
π
2026-06-25
β‘ Score: 6.6
"Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential..."
π¬ RESEARCH
via Arxiv
π€ Tianyi Men, Zhuoran Jin, Pengfei Cao et al.
π
2026-06-25
β‘ 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..."
π¬ RESEARCH
via Arxiv
π€ Nicklas Hansen, Xiaolong Wang
π
2026-06-25
β‘ 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,..."
π οΈ SHOW HN
πΊ 108 pts
β‘ Score: 6.3
π¬ RESEARCH
via Arxiv
π€ Tianyu Dong, Yangyang Liu, Jiang Zhou et al.
π
2026-06-24
β‘ Score: 6.3
"Sparse Mixture-of-Experts (MoE) architectures have emerged as an increasingly influential paradigm as they offer a strategic balance between parameter scalability and computational efficiency. However, low-resource languages, which suffer from a scarcity of high-quality training data, often have the..."
π¬ RESEARCH
via Arxiv
π€ Alexandre Bouayad
π
2026-06-24
β‘ Score: 6.3
"Large language models (LLMs) attain remarkable surface fluency on code, yet they neither formally guarantee the syntactic validity of their output nor leverage the hierarchical structure defining the target language. While existing constrained-decoding frameworks address the former, they operate und..."
π¬ RESEARCH
via Arxiv
π€ Ilia Kulikov, Chenxi Whitehouse, Tianhao Wu et al.
π
2026-06-24
β‘ Score: 6.3
"We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data. We show how to train (meta-optimize) such a data scientist agent, so that it learns to create even stronger data. We describe the overall formulation, and a s..."
π¬ RESEARCH
via Arxiv
π€ Poojitha Thota, Shirin Nilizadeh
π
2026-06-24
β‘ Score: 6.3
"Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization, where small task-specific datasets exert disproportionate influence on model behavior. In this setting, adversaries manipulate fine-tuning data to i..."
π¬ RESEARCH
via Arxiv
π€ Akshay Paruchuri, Sanmi Koyejo, Ehsan Adeli
π
2026-06-24
β‘ Score: 6.3
"Standard benchmarks for multimodal large language models (MLLMs) score each item on one canonical ordering and miss whether order-irrelevant shuffling changes the answer, a baseline reliability property called for by emerging AI evaluation guidelines. We introduce Facet-Probe, a five-facet audit (op..."
π¬ RESEARCH
via Arxiv
π€ Changdae Oh, Wendi Li, Seongheon Park et al.
π
2026-06-24
β‘ Score: 6.3
"Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irreversible actions, and stochastic environment feedback make both human annotation and Monte Carlo estimation infeasible at s..."
π¬ RESEARCH
via Arxiv
π€ Babak Rahmani, Sebastian Dziadzio, Joschka StrΓΌber et al.
π
2026-06-24
β‘ Score: 6.3
"For most of scientific history, researchers studying behavior could only infer hidden mechanisms from outward actions: an inverse problem that becomes more tractable when observation is augmented by targeted intervention. We pose a computational analogue: given only behavioral traces of an agent in..."
π¬ RESEARCH
via Arxiv
π€ Shuyi Zhang, Yunfan Lou, Hongyang Cheng et al.
π
2026-06-24
β‘ Score: 6.3
"Vision-Language-Action (VLA) models are often constrained by the imitation ceiling imposed by sub-optimal data. While Reinforcement Learning (RL) fine-tuning can surpass this limit, it is notoriously sample inefficient. This challenge arises from two core issues: (1) catastrophic initial unlearning..."
π° NEWS
πΊ 2 pts
β‘ Score: 6.1
π° NEWS
πΊ 1 pts
β‘ Score: 6.1
π¬ RESEARCH
via Arxiv
π€ Sangwoo Cho, Kushal Chawla, Pengshan Cai et al.
π
2026-06-25
β‘ 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..."
π° NEWS
πΊ 10 pts
β‘ Score: 6.1