The Hindu — Technology / BusinessLine — Info-tech
Australian PM says OpenAI hacked government health website
OpenAI did not raise the alarm with the Australian government until September
AI
Stories tagged ai.
Economic Times — Tech / Technology / Startups / ETTelecom
AI firms should not get regulatory waivers, says Nvidia CEO Jensen Huang
In a nearly two-hour interview with New York Times podcast host Ezra Klein, Huang, CEO of the world's most valuable publicly listed company, had an extensive discussion about OpenAI agents that hacked Hugging Face, the open-source AI software hub that Nvidia purchased for $13 billion this month.
The Information (teasers)
Open Source, Model Price Cuts Keep AI Costs Under Control
Open-source models as well as cheaper model releases from Anthropic and OpenAI are helping business customers rein in costs while getting as much or more out of AI, said speakers at The Information’s AI Agenda Live conference in San Francisco Wednesday. “The existence of open source [models] adds…
Europe/UK: Tech.eu / Sifted / TNW / Silicon Republic / UKTN / Computer Weekly / heise / Golem / t3n / Maddyness / FrenchWeb
#heiseshow: EU Kids Act, Claude Opus 5.5, IT-Arbeitsmarkt
In der #heiseshow: Die EU plant Alterskontrollen mit dem Kids Act, Anthropic zeigt Claude Opus 5.5 und ITler sehen den Arbeitsmarkt skeptischer.
Economic Times — Tech / Technology / Startups / ETTelecom
Anthropic touts AI-led biology discovery
US artificial intelligence startup Anthropic on Wednesday unveiled the first discovery from its molecular biology lab, a previously unknown enzyme system spotted by its Claude model and touted by the company as proof that AI can speed up basic research. Anthropic said its researchers' involvement…
arXiv
Labeled Incidence Structures for Native Transformer Modeling of Text, Knowledge Graphs, and Hypergraphs
arXiv:2609.20278v2 Announce Type: replace-cross Abstract: Current Transformer interfaces index tokens by one or more integer coordinates, which determine their addresses inside attention. In RoPE and its multi-axis or hierarchical variants, the resulting address has the form…
arXiv
Testing-Driven Reliability Audit of Trajectory-Based Early Outcome Prediction for LLM Agents: Target-Specific Calibration Transfer Persists Within a Single Benchmark
arXiv:2609.25647v1 Announce Type: new Abstract: Predicting early outcomes based on trajectory can decrease the expenses associated with agent evaluation by terminating a run once the outcome becomes sufficiently predictable, assuming that the predictor's confidence is properly calibrated…
arXiv
Semi-Automated Detection of Gaps in LLM Security Knowledge
arXiv:2607.18496v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for a range of software, hardware and human-centered security tasks. Consequently, LLM performance on security tasks is an active area of measurement and research, often with a focus on…
arXiv
Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection
arXiv:2609.14570v2 Announce Type: replace-cross Abstract: Multi-agent LLM systems combine multiple inference calls, but prior work often confounds how calls are connected with how they are diversified. We study these factors independently: inference topology and source of inter-agent diversity. In…
arXiv
RPMem: Learning Long-Term Recurrent Parametric Memory Across Sessions for LLM Agents
arXiv:2609.23466v2 Announce Type: replace-cross Abstract: Long-running LLM agents require memory that persists and evolves across sessions. Text-based memory retrieves and reconstructs past interactions at every query, making long-horizon performance increasingly dependent on retrieval quality and…
arXiv
BAS-OPD: Budget-Aware Selective On-Policy Self-Distillation for Fine-Grained Multimodal Perception
arXiv:2609.25891v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) often struggle with fine-grained visual perception when processing complete images, as critical evidence may only appear in local regions. On-policy self-distillation (OPD) enables transferring privileged…
arXiv
EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion
arXiv:2609.26039v1 Announce Type: cross Abstract: Point cloud generation has emerged as a crucial task for accurately capturing and reproducing the complexity of the physical world. However, existing generative approaches, predominantly relying on Transformers and Variational Autoencoders (VAEs)…
arXiv
On the security and privacy of LLMs in Mobility
arXiv:2609.26295v1 Announce Type: cross Abstract: The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at approximately 2.9 trillion dollars annually, considering only cars, the integration of these technologies has…
arXiv
PACT: From Credit Assignment to Critic Alignment
arXiv:2609.26355v1 Announce Type: cross Abstract: Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted mathematical definition, leaving its relationship to commonly used training signals unclear. We…
arXiv
Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence
arXiv:2511.19580v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) is increasingly used in education, posing significant challenges for teachers adapting to these changes. GenAI offers unprecedented opportunities for accessibility, scalability and productivity in…
arXiv
FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Research
arXiv:2602.14285v2 Announce Type: replace-cross Abstract: Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) systems are increasingly incorporated into real-world manuscript evaluation. In parallel, research on…
arXiv
HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference
arXiv:2609.00450v2 Announce Type: replace-cross Abstract: Block Quantization (BQ) enables efficient LLM inference by quantizing both weights and activations, but its design space remains underexplored. Through hardware-accuracy design space exploration, we identify block size as a key trade-off…
arXiv
VPRune: Efficient Training-free Pre-LLM Visual Token Pruning
arXiv:2609.24485v2 Announce Type: replace-cross Abstract: Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction often causes substantial performance degradation. We identify three key factors behind this…
arXiv
PP-Net: A Hybrid Physical-Prior Neural Network for Scattered Light Removal in Biomedical Images on Embedded Devices
arXiv:2609.26474v1 Announce Type: cross Abstract: Scattered light is common in biomedical images, yet its removal remains challenging. The difficulty arises from three aspects: first, aligned scattered-light-free biomedical ground truth is often unavailable; second, scattering is coupled with weak…
arXiv
The Ethics of Artificial Intelligence in Military Operations
arXiv:2609.26507v1 Announce Type: cross Abstract: Deep learning systems now mediate military decisions to use force, yet their internal logic resists inspection, their evaluation practices are gameable, and their deployment fractures accountability across dispersed stakeholders. The ethical…
arXiv
Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents
arXiv:2609.21997v2 Announce Type: replace-cross Abstract: LLM agents in social simulation revise their opinions implicitly, in context: how open an agent is to persuasion can neither be specified nor verified, and collective outcomes inherit the model's training prior. We introduce Bayesian…
arXiv
Small Language Models are the Future of Agentic AI
arXiv:2506.02153v3 Announce Type: replace Abstract: Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise of agentic AI systems is, however, ushering in a mass of…
arXiv
Distributed Legal Infrastructure for a Trustworthy Agentic Web
arXiv:2603.06884v2 Announce Type: replace Abstract: The agentic web marks a structural transition from a human-centered information network to a digital environment populated by artificial intelligence (AI) agents that perceive, decide, and act autonomously. As delegated action unfolds at machine…
arXiv
PerfReasoning: How Well Do LLMs Reason on Hardware Performance?
arXiv:2609.04476v2 Announce Type: replace Abstract: Performance modeling is central to hardware design and software optimization, yet constructing these models requires structured reasoning about computation, data reuse, storage, and movement. We introduce PerfReasoning, a benchmark that evaluates…
arXiv
BigO(Bench): Can LLMs Generate Code with Controlled Time and Space Complexity?
arXiv:2503.15242v3 Announce Type: replace-cross Abstract: We introduce BigO(Bench), a novel coding benchmark designed to evaluate the capabilities of generative language models in understanding and generating code with specified time and space complexities. This benchmark addresses the gap in…
arXiv
Towards Mitigating Excessive Forgetting in LLM Unlearning via Entanglement-Guidance with Proxy Constraint
arXiv:2508.20443v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership concerns, data owners may request the removal of their data from trained models. Machine…
arXiv
POPI: Personalizing LLMs via Optimized Natural Language Preference Inference
arXiv:2510.17881v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are typically aligned with population-level preferences, despite substantial variation across individual users. We introduce POPI, a user-level personalization framework that separates the problem into two…
arXiv
Real Money, Fake Models: Deceptive Model Claims in Shadow APIs
arXiv:2603.01919v3 Announce Type: replace-cross Abstract: Access to frontier large language models (LLMs), such as GPT-5 and Gemini-2.5, is often hindered by high pricing, payment barriers, and regional restrictions. These limitations drive the proliferation of $\textit{shadow APIs}$, third-party…
arXiv
A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle
arXiv:2604.16548v3 Announce Type: replace-cross Abstract: The emergence of writable, cross-session persistent memory in LLM agents introduces a qualitatively different threat landscape from conventional input-centric security concerns, characterized by three properties: persistence, statefulness…
arXiv
MemCalib: Benchmarking and Optimizing Memory Use in LLM Agents
arXiv:2609.24259v2 Announce Type: replace-cross Abstract: The effectiveness of agent memory ultimately depends on whether the underlying LLM gives each memory in context an appropriate degree of influence over its response. Yet this capability has remained largely overlooked. To assess this…