Knowledge catalogue
arXiv:2609.11524v1 Announce Type: cross Abstract: Many applications in engineering, including wireless broadcasting, require designs that provide performance certificates at different target outage le
arXiv:2609.11433v1 Announce Type: new Abstract: Skill adaptation frameworks based on reinforcement learning often require restrictive assumptions to maintain stability, such as fixed observations or t
arXiv:2609.09219v1 Announce Type: cross Abstract: AI research agents combine prior knowledge, public sources, and experimental feedback to produce useful results. The Discovery Certification Protocol
arXiv:2609.11753v1 Announce Type: new Abstract: Imitation learning for dexterous hands is bottlenecked by the difficulty of collecting contact-rich demonstrations that transfer faithfully to the robot
arXiv:2609.11929v1 Announce Type: new Abstract: We launch SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free
arXiv:2609.10644v1 Announce Type: cross Abstract: Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than re
arXiv:2505.23942v2 Announce Type: replace Abstract: Prevailing activation functions such as Swish and GELU tend toward domain-specific optima, Swish was discovered via neural architecture search on vi
On September 11, 2026, Gary Marcus tweeted that a CNN eLive session with Lauren Coates would air at approximately 11:30 PM ET to discuss whether generative AI should be boycotted. The tweet includes t
arXiv:2609.11752v1 Announce Type: cross Abstract: For industrial content risk control, the real deployment constraint is not average accuracy but how much risk can be auto-handled under high precision
arXiv:2512.06956v2 Announce Type: replace-cross Abstract: Inverse reinforcement learning aims to infer the reward function that explains expert behavior observed through trajectories of state--action
arXiv:2603.11578v2 Announce Type: replace Abstract: Simultaneous machine translation (SiMT) has traditionally relied on offline machine translation models coupled with human-engineered heuristics or l
arXiv:2609.09928v1 Announce Type: new Abstract: Latent reasoning approaches enhance token-level efficiency and robustness by replacing verbose, explicit chain-of-thought (CoT) tokens with compact cont
arXiv:2609.11382v1 Announce Type: new Abstract: Aerial robot swarms have the potential to transform time-critical safety, security, and search-and-rescue operations. By coordinating multiple robots, t
arXiv:2609.11838v1 Announce Type: new Abstract: Cardiovascular screening models trained on national health surveys routinely report areas under the receiver operating characteristic curve (AUROC) near
arXiv:2609.10951v1 Announce Type: cross Abstract: AI-based automated vehicle testing is challenging because a model that passes every test condition can still fail in the real world. Formal verificati
Thanks to @cnni for letting me speak from the heart. We covered a lot in a short clip, from Anthropic’s good and bad points to the possible need for a boycott and the need for international agreements
arXiv:2609.11724v1 Announce Type: new Abstract: This paper details the Eloquence team's approach to Task 2 of the 2nd MLC-SLM challenge at Interspeech 2026, which involves multilingual Multiple-Choice
The Hugging Face incidents has already proved that you can have harmful, infrastructure-attacking AI without getting to AGI, RSI, or superintelligence, let alone the so-called Singularity. And already
arXiv:2609.11146v1 Announce Type: cross Abstract: AI-generated text is flowing back into the training corpora of the next generation of models. Recursive training on it drives model collapse, and rece
Astra's launch has produced a strange discourse. The reporting that broke the story framed the model's use of recurrent depth primarily as a safety regression, because it means the model reveals less
arXiv:2512.10835v2 Announce Type: replace Abstract: This paper introduces a reinforcement learning framework that enables controllable and diverse player behaviors without relying on human gameplay da
arXiv:2609.10613v1 Announce Type: cross Abstract: In-context learning (ICL) jailbreaks reveal a critical vulnerability in multimodal large language models (MLLMs): harmful demonstrations in the prompt
arXiv:2609.11549v1 Announce Type: new Abstract: European Union (EU) policymakers adopted revolutionary data collection provisions for Automated Driving Systems (ADS) in the recently approved regulatio
arXiv:2609.09263v1 Announce Type: cross Abstract: Speech-to-speech (S2S) models now run inside dubbing, translation, and voice agents. Unlike text models, they hear the speaker's voice, which carries
arXiv:2609.11166v1 Announce Type: new Abstract: Rapid evaluation of many simultaneous road-link disruptions requires a practical compromise between exact spectral recomputation and local approximation
arXiv:2609.10961v1 Announce Type: new Abstract: P300 brain-computer interface (BCI) spellers can provide hands-free communication for people with severe motor impairments. Modern pipelines combine mul
arXiv:2609.11067v1 Announce Type: new Abstract: Large language models are increasingly used as judges to measure social bias in text, yet the passages they judge are often noisy, containing typos, inf
arXiv:2609.10221v2 Announce Type: new Abstract: Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe
arXiv:2609.11310v1 Announce Type: new Abstract: We address few-shot object detection with vision-language models (VLMs) in out-of-domain settings such as aerial, industrial, and medical imagery, using
arXiv:2609.10339v1 Announce Type: new Abstract: A confidence-aware multimodal fusion framework (CAMF) is proposed to realize reliable human intention prediction for industrial human-robot collaboratio
arXiv:2109.02355v2 Announce Type: replace-cross Abstract: The last decade of progress in machine learning (ML), especially the deep learning era, has raised a number of scientific questions that chall
arXiv:2609.08016v1 Announce Type: new Abstract: Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagree
arXiv:2609.09650v1 Announce Type: new Abstract: Reinforcement learning (RL) has demonstrated considerable potential for autonomous driving decision-making. However, its deployment in urban autonomous
arXiv:2609.10084v1 Announce Type: cross Abstract: Generalized zero-shot learning (GZSL) has emerged as an important paradigm for visual recognition systems that must generalize to classes that were no
arXiv:2609.06708v1 Announce Type: cross Abstract: Large language models are transforming many industries with their text generation abilities. However, their outputs can be easily tampered with, creat
arXiv:2609.06543v1 Announce Type: new Abstract: Enterprise AI is evolving into an Enterprise Operating System where autonomous AI agents can plan, reason, use memory, invoke tools, execute workflows,
arXiv:2609.09855v1 Announce Type: cross Abstract: Although probabilistic statements are ubiquitous, foundational disagreements persist about their understanding, as exemplified by debates between Baye
arXiv:2609.08256v1 Announce Type: cross Abstract: Automated red-team attacks and blue-team defenses for large language models (LLMs) are advancing quickly. However, attackers and defenders are built a
arXiv:2609.09492v1 Announce Type: new Abstract: Reinforcement learning has produced capable controllers across a broad range of legged-robot tasks, but a subset of these tasks fail to converge under s
arXiv:2603.08275v2 Announce Type: replace-cross Abstract: With the global proliferation of Large Language Models (LLMs), cultural safety, defined as the ability to generate respectful and appropriate
arXiv:2609.10456v1 Announce Type: new Abstract: Nanoscale imaging of mammalian brains is critical for connectomics. X-ray laminography enables high-throughput imaging of extended, plate-like biologica
arXiv:2609.05837v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundame
arXiv:2609.05995v1 Announce Type: new Abstract: Achieving reliable autonomy in the wild requires agents to sustain continuous operations across long-horizon trajectories. However, as agents navigate t
arXiv:2609.09187v1 Announce Type: cross Abstract: Advertising video generation is not only a video synthesis task, but also a product-conditioned reasoning problem whose success is measured by online
👇 All this Doom talk is a distraction from the fact that the big frontier AI companies (especially OpenAI) aren’t doing security competently. As an email I just got from longtime security engineer @Ni
arXiv:2609.07152v1 Announce Type: new Abstract: Goal-oriented conversational systems must answer factual questions, understand visitor-provided information, and advance business objectives without bec
arXiv:2609.08729v1 Announce Type: new Abstract: The transition from single-core to multi-core architectures in safety-critical embedded systems introduces significant challenges due to inter-core inte
arXiv:2609.09429v1 Announce Type: new Abstract: While accurate measurement of socioeconomic indicators remains challenging in data-scarce regions, which limits policy interventions and resource alloca
arXiv:2609.10193v1 Announce Type: cross Abstract: Protein-protein interaction (PPI) databases do not faithfully reflect biological realities. Instead, they are influenced by study and technical biases
arXiv:2609.06783v1 Announce Type: cross Abstract: LLM agents operate in workflows where unsafe actions can have real consequences. Existing safety evaluations often reduce behavior to a single score,
arXiv:2609.10024v1 Announce Type: new Abstract: Robot Operating System 2 (ROS 2) standardizes application code against a middleware interface (RMW) whose reference implementations are built on the Dat
arXiv:2609.05794v1 Announce Type: cross Abstract: Open-weight large language models face a low-cost white-box threat from representation engineering attacks. Attackers can estimate refusal directions
arXiv:2609.10036v1 Announce Type: cross Abstract: Large language model agents produce fluent action sequences across a wide range of tasks, yet they fail in characteristic ways once the environment be
arXiv:2602.14488v3 Announce Type: replace-cross Abstract: IR in low-resource languages remains limited by the scarcity of high-quality, task-specific annotated datasets. Manual annotation is expensive
arXiv:2505.13520v2 Announce Type: replace-cross Abstract: Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances have
arXiv:2609.05437v1 Announce Type: new Abstract: Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., `
arXiv:2609.09783v1 Announce Type: new Abstract: Asynchronous reinforcement learning has become the standard way to scale training for language models, but the resulting policy lag biases the critic to
arXiv:2609.08188v1 Announce Type: new Abstract: Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers
arXiv:2408.10608v2 Announce Type: replace Abstract: Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary prom
arXiv:2609.08602v1 Announce Type: new Abstract: Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequence