January 2026

ICML 2026 will follow a two-policy framework for the use of large language models (LLMs) in reviewing, based on the following two policies: Policy A (Conservative): Use of LLMs for reviewing is strictly prohibited. Policy B (Permissive): Allowed: Use of LLMs to help understand the paper and related works, and polish reviews. Submissions can be fed to privacy-compliant* LLMs. Not allowed: Ask LLMs about strengths/weaknesses, ask to suggest key points for the review, suggest an outline for the […]

Ver mais

Large Reasoning Models (LRMs) achieve explicit chain-of-thought expansion by imitating deep thinking behaviors of humans, demonstrating excellent performance in complex task scenarios. However, the deep-thinking mode often leads to unnecessarily lengthy reasoning and resource inefficiency when handling simple tasks. This overthinking phenomenon may arise from the generation preference triggered by the reward function during post-training. Existing research attempts to mitigate overthinking from the perspective of prompt design or model training, but generally underestimates the importance of task difficulty […]

Ver mais

We introduce Statsformer, a principled framework for integrating large language model (LLM)-derived knowledge into supervised statistical learning. Existing approaches are limited in adaptability and scope: they either inject LLM guidance as an unvalidated heuristic, which is sensitive to LLM hallucination, or embed semantic information within a single fixed learner. Statsformer overcomes both limitations through a guardrailed ensemble architecture. We embed LLM-derived feature priors within an ensemble of linear and nonlinear learners, adaptively calibrating their influence via cross-validation. This […]

Ver mais

The Job-Shop Scheduling Problem (JSSP), under various forms of manufacturing uncertainty, has recently attracted considerable research attention. Most existing studies focus on parameter uncertainty, such as variable processing times, and typically adopt the actor-critic framework. In this paper, we explore a different but prevalent form of uncertainty in JSSP: structural uncertainty. Structural uncertainty arises when a job may follow one of several routing paths, and the selection is determined not by policy, but by situational factors (e.g., the […]

Ver mais

Backpropagation algorithm has driven the remarkable success of deep neural networks, but its lack of biological plausibility and high computational costs have motivated the ongoing search for alternative training methods. Hebbian learning has attracted considerable interest as a biologically plausible alternative to backpropagation. Nevertheless, its exclusive reliance on local information, without consideration of global task objectives, fundamentally limits its scalability. Inspired by the biological synergy between neuromodulators and local plasticity, we introduce a novel model-agnostic Global-guided Hebbian Learning […]

Ver mais

A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. However, standard reward models are susceptible to spurious features that are not causally related to human labels. This can lead to reward hacking, where high predicted reward does not translate into better behavior. In this work, we address this problem from a causal perspective by proposing a factored representation learning framework that decomposes the model’s contextual embedding into […]

Ver mais

How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Claude Code Launches Teleport Workflow: Start Anywhere, Continue Everywhere By @proflead [ 4 Min read ] Read More. AI Doesn’t Mean the End of Work for Us By @bernard [ 4 Min read ] I believe that AI’s impact and future pathways are overstated […]

Ver mais

Urban Air Mobility (UAM) has emerged as a transformative solution to alleviate urban congestion by utilizing low-altitude airspace, thereby reducing pressure on ground transportation networks. To enable truly efficient and seamless door-to-door travel experiences, UAM requires close integration with existing ground transportation infrastructure. However, current research on optimal integrated routing strategies for passengers in air-ground mobility systems remains limited, with a lack of systematic exploration.To address this gap, we first propose a unified optimization model that integrates strategy […]

Ver mais

Following up on earlier discussions around AI evals and static guarantees. In some recent work, we looked at G-CTR-style approaches and tried to understand where they actually help in practice — and where they quietly fail. A few takeaways that surprised us: – static guarantees can look strong while missing adaptive failure modes – benchmark performance ≠ deployment confidence – some failure cases only show up when you stop optimizing the metric itself Paper for context: https://arxiv.org/abs/2601.05887 Curious […]

Ver mais

Key Highlights: OpenAI has been among the key players in the AI industry, or let’s just say everyone’s favorite, except for Elon Musk. Many tech giants have invested in the company, including Microsoft in its initial days. Apparently, the bet taken by Satya Nadella, for which Bill Gates was skeptical, has been turning out fruitful for the Redmond giant. OpenAI reportedly set to receive massive investments from NVIDIA, Netflix, and Amazon Now, according to The Information, NVIDIA, Microsoft, […]

Ver mais