Knowledge catalogue
arXiv:2608.20851v1 Announce Type: cross Abstract: Agentic engineering systems have shown strong performance on general-purpose benchmarks, yet their effectiveness in enterprise resource planning (ERP)
Be sure to tune in this Wednesday! Inference Hours is back with another Kimi K3 webinar! On Wednesday, August 26, Zain Hasan sits down with Kevin Cui and Austin Silveria from Together AI's inference t
arXiv:2608.20975v1 Announce Type: new Abstract: Effective social interaction requires agents to translate mental state inferences into coordinated behavioral signals across verbal and nonverbal channe
My test is running DeepSeek v4 Flash 0731 at native size on DGX Spark machine (GB10, 128 GB unified memory). The model size is bigger than RAM, so weights will be loaded many times when running. To im
Graph: A cumulative metric of Speed x Context Size - the higher the dot - the better. Helpful for looking for the most balanced solution. The research relies on DFlash2 Q2 work by AnalogAlok: https://
arXiv:2510.16211v2 Announce Type: replace Abstract: Label noise is a common problem in real-world datasets, affecting both model training and validation. Clean data are essential for achieving strong
arXiv:2608.21249v1 Announce Type: new Abstract: While recent large language models (LLMs) have achieved promising results on individual patent drafting tasks, they fundamentally fail to investigate th
arXiv:2608.20497v1 Announce Type: new Abstract: Deploying high-accuracy neural networks on resource-constrained edge devices remains challenging, as existing approaches treat training, compression, an
Share what your favorite models are right now and why. Given the nature of the beast in evaluating VLMs (untrustworthiness of benchmarks, immature tooling, intrinsic stochasticity), please be as detai
Hi everybody! Every now and then these days, we’re seeing really huge open-weight models popping up. But since not everybody has a DGX Station at home, I’m interested in really small models. It’s incr
arXiv:2608.20649v1 Announce Type: new Abstract: Designers and policymakers in sociotechnical domains like content moderation, privacy interfaces, recommender systems and beyond, must choose among a gr
arXiv:2608.20563v1 Announce Type: cross Abstract: Long-horizon security LLM agents must carry information and decisions across many dependent interactions, where later actions often depend on services
arXiv:2608.20768v1 Announce Type: new Abstract: Specialist language models are usually understood through endpoint gains: the generalist scores lower, the specialist scores higher, and the difference
arXiv:2506.10903v2 Announce Type: replace Abstract: Statement autoformalization plays a crucial role in formal mathematical reasoning by enabling the automatic translation of natural language statemen
arXiv:2608.21204v1 Announce Type: cross Abstract: Behaviour Cloning (BC) has driven remarkable progress in robot manipulation, yet it is fundamentally limited by its inability to self-improve: a polic
arXiv:2410.14874v3 Announce Type: replace Abstract: Multi-Head Self-Attention (MHSA) is the cornerstone of Vision Transformers, allowing models to capture diverse feature representations by projecting
arXiv:2608.20349v1 Announce Type: cross Abstract: Large Language Models (LLMs) exhibit extreme sensitivity to surface-level prompt variations, in which minor lexical changes can trigger disproportiona
arXiv:2608.20344v1 Announce Type: new Abstract: LLM-based 'digital twins' aim to simulate how an individual would behavein new environments or respond to novel questions, given some representation of
arXiv:2608.20427v1 Announce Type: cross Abstract: Dense causal attention remains expensive at long context even when implemented with highly optimized exact kernels. We study BF1, a deterministic bloc
arXiv:2511.08142v2 Announce Type: replace Abstract: Federated Learning (FL) is a promising machine learning solution in large-scale IoT systems, guaranteeing load distribution and privacy. However, FL
arXiv:2608.20884v1 Announce Type: new Abstract: Face recognition systems (FRSs) are increasingly deployed in critical real-world services for authentication, such as banking applications and airport i
arXiv:2608.20691v1 Announce Type: new Abstract: Panoramic image generation is increasingly important for immersive applications such as virtual reality, augmented reality, and 3D content creation. Unl
Learn how to build a voice ordering system for restaurants that answers a phone call and takes an order end to end, with no app, no website, and no sign-in. It uses Amazon Connect for telephony, Amazo
arXiv:2608.20346v1 Announce Type: new Abstract: Speech systems used in customer-facing applications often require domain-specific language coverage. We present a synthetic Bengali speech dataset for t
arXiv:2608.20667v1 Announce Type: cross Abstract: Pseudo-label-based semi-supervised learning has achieved strong performance due to its simplicity and scalability. However, it is typically developed
arXiv:2608.20729v1 Announce Type: new Abstract: Language-model agents can improve after failure or carry text across episodes without revising what counts as success. We study the narrower attribution
arXiv:2608.21089v1 Announce Type: new Abstract: Integrating Large Language Models (LLMs) into the Indian judiciary promises access to justice but introduces severe risks. We identify the 'inertia of c
arXiv:2608.20960v1 Announce Type: new Abstract: Language models (LMs) are trained on static scientific corpora, whereas scientific knowledge continuously evolves through correction and revision. Scien
arXiv:2411.08881v3 Announce Type: replace-cross Abstract: AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bia
arXiv:2506.13583v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) agents often exhibit learning behaviors that are not intuitively interpretable by human observers, which can resul
arXiv:2608.21147v1 Announce Type: new Abstract: The cyclic structure of physiological processes offers a natural prior for self-supervised representation learning, and the cardiac cycle provides a par
arXiv:2608.20763v1 Announce Type: cross Abstract: Linear probes and activation steering have uncovered that vision-language models (VLMs) internally represent mental states such as agents' beliefs, kn
arXiv:2608.20771v1 Announce Type: new Abstract: Search Agents face a severe reliability crisis during reinforcement learning (RL) fine-tuning. Heuristic Top-K retrieval often causes critical evidence
arXiv:2608.20420v1 Announce Type: new Abstract: This paper develops a phenomenology-first approach to artificial consciousness by reframing consciousness as the subjective experience enacted through a
arXiv:2608.21079v1 Announce Type: new Abstract: Adverse Pregnancy Outcomes (APOs) such as preterm birth and gestational diabetes can have long-term consequences for both the mother and child, yet an u
arXiv:2608.20686v1 Announce Type: new Abstract: Many scientific discovery problems require searching combinatorial hypothesis spaces under complex domain constraints. Reinforcement learning (RL) offer
arXiv:2608.21060v1 Announce Type: new Abstract: Pathology foundation models (PFMs) are increasingly used as general-purpose backbones, yet existing benchmarks cannot systematically diagnose their whol
arXiv:2608.20820v1 Announce Type: new Abstract: Large language models (LLMs) are vulnerable to multi-turn jailbreak attacks that progressively manipulate conversation context. Existing certified robus
arXiv:2608.20791v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies are vulnerable to localized physical perturbations, yet existing certified patch defenses target discrete labels
arXiv:2601.13798v3 Announce Type: replace-cross Abstract: Language-aligned vision foundation models perform strongly across diverse downstream tasks. Yet, their learned representations remain opaque,
Check out these talks from last month's Nerd Meetup, this time at @posthog's office in SF. @Runloop - Coordinating shared state for multiplayer agents http://Reboot.dev - How the architecture of code
arXiv:2608.21114v1 Announce Type: cross Abstract: Visual world-model agents such as DreamerV3 act through a recurrent latent state rather than a single observation, which weakens frame-wise observatio
arXiv:2608.20357v1 Announce Type: cross Abstract: Deep search is brittle on underspecified user queries: missing constraints such as time, location, scope, or definitions can lead to retrieval drift a
arXiv:2608.21101v1 Announce Type: cross Abstract: As large language model (LLM) agents move from conversation to executing code, reading local files, and orchestrating external tools, a single agent h
arXiv:2608.21278v1 Announce Type: new Abstract: Improving the safety of large language models (LLMs) often comes at the expense of utility, as globally applied safety tuning may affect model responses
arXiv:2608.21055v1 Announce Type: cross Abstract: Collaborative perception extends the sensing range of a single vehicle by fusing observations from nearby agents, which improves the robustness of aut
arXiv:2608.21142v1 Announce Type: new Abstract: Structured pruning reduces the size and inference cost of large language models (LLMs) by removing weight columns, but the resulting output error can de
arXiv:2606.15867v2 Announce Type: replace Abstract: Multi-subject reference-based image generation requires jointly preserving multiple human identities, binding per-person objects and fashion items,
arXiv:2608.21030v1 Announce Type: cross Abstract: Video multimodal large language models have advanced significantly, yet fine-grained motion-temporal understanding remains fragile. The core bottlenec
Coming to Connected Stack in SF — @jerryjliu0, CEO & Co-founder of @llama_index, nominated by @GreylockVC. LlamaIndex is building document infrastructure for AI agents, with LlamaParse purpose-built f
arXiv:2608.21277v1 Announce Type: new Abstract: State-of-the-art multivariate time-series forecasters can model complex temporal and cross-variable dependencies, yet their opaque representations provi
arXiv:2608.20562v1 Announce Type: cross Abstract: Mixed-type data containing both numerical and categorical variables arise in many scientific and real-world applications. Existing representation lear
arXiv:2608.20564v1 Announce Type: new Abstract: Multi-agent LLM systems can improve reasoning by pooling diverse perspectives, but their effectiveness depends on coordinating communication, particular
arXiv:2608.20561v1 Announce Type: cross Abstract: Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensiv
arXiv:2608.20738v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are widely used on graph-structured data, but most suffer from two key weaknesses. First, message passing behaves as a low-
Control is becoming the defining topic in enterprise AI adoption. Across the world, and especially in regulated industries, organizations want the benefits of AI without giving up control over their s
arXiv:2608.21041v1 Announce Type: cross Abstract: Geospatial representation learning from satellite imagery is a fundamental problem for large-scale urban analysis and real-world applications. Despite
arXiv:2608.20864v1 Announce Type: new Abstract: Artificial Intelligence (AI) offers significant potential for future aviation systems; however, its integration into safety-critical applications requir
arXiv:2504.11055v3 Announce Type: replace Abstract: Zero-shot anomaly detection and localization aims to learn from source-domain data and generalize to unseen target domains without target-domain sam
arXiv:2606.13892v2 Announce Type: replace-cross Abstract: The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exa