Knowledge catalogue
arXiv:2609.39975v1 Announce Type: cross Abstract: This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluati
Qodo 3.0 makes a statement: Engineering teams want to build their own software factory, not buy a black box. @QodoAI provides the quality, governance, plus a map of your software and your team’s triba
arXiv:2602.13194v3 Announce Type: replace-cross Abstract: Humans and large language models can predict next letter or word from its prior context much better than random guessing, indicating strong re
arXiv:2609.38397v1 Announce Type: new Abstract: Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation. H
arXiv:2512.07011v2 Announce Type: replace-cross Abstract: Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention's quadratic complexity c
Today, Google Cloud Data Agent Kit is generally available. Data Agent Kit is a free set of Model Context Protocol (MCP) tools and agent skills that lets the coding agent you already use work directly
arXiv:2602.11328v2 Announce Type: replace Abstract: As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential. In this work, we focus on behavioral disp
arXiv:2609.36314v1 Announce Type: cross Abstract: State Space Models (SSMs) compress sequence history into a bounded recurrent state, making the resulting memory law a central architectural choice for
arXiv:2609.36278v1 Announce Type: new Abstract: Generative agent-based models (GABMs) are increasingly used to simulate social media dynamics, including misinformation spread. For such social simulati
arXiv:2609.38058v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on i
arXiv:2609.36108v1 Announce Type: cross Abstract: Tabular foundation models using in-context learning have recently surpassed gradient-boosted trees on predictive tabular tasks. However, recent mechan
arXiv:2609.36322v1 Announce Type: cross Abstract: Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer
arXiv:2609.36984v1 Announce Type: new Abstract: Complex questions often require multi-hop reasoning that connects facts distributed across sources or distant regions of a long context through intermed
Generated with H3: Mock H3 version of the ending to Wargames(83) as a test of a custom node I've been working on w/ both Claude(Opus/Fable) and OAI's Sol/Astra. One click generation using only 4 movie
arXiv:2609.36742v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for improving large language models (LLMs) on various tasks, yet it
arXiv:2609.37035v1 Announce Type: new Abstract: Streaming video assistance requires models to answer asynchronous questions from an observed prefix under a fixed context budget. Existing approaches mo
arXiv:2609.31713v1 Announce Type: new Abstract: As large language models (LLMs) become capable of processing increasingly diverse modalities and longer temporal contexts, an emerging line of work is m
arXiv:2609.32870v1 Announce Type: cross Abstract: Self-play proposer--solver methods improve reasoning by generating tasks and learning from verified solutions. However, for evidence-identifiable task
arXiv:2609.32805v1 Announce Type: cross Abstract: Long tasks produce more history than an LLM agent can hold in its context, and more than it uses reliably even when the history fits. A growing line o
arXiv:2609.33405v1 Announce Type: new Abstract: Autoregressive pretraining increasingly draws on heterogeneous data, making it important to understand how a model learns from an individual token. The
arXiv:2609.35616v1 Announce Type: new Abstract: Interactive 3D head generation requires coordinated speaking and listening motion that responds to an evolving conversation. Existing generators use inc
arXiv:2609.33410v1 Announce Type: new Abstract: Autoregressive decode repeatedly streams a growing KV cache, making attention a major cost at long context. Existing high-performance kernels use online
arXiv:2505.17458v3 Announce Type: replace Abstract: Machine learning on heterogeneous graphs has advanced rapidly, driven by the diverse entities, relations, and semantics inherent in real-world data.
arXiv:2609.34731v1 Announce Type: cross Abstract: We develop an information-theoretic framework for generalization in next-token prediction under temporally dependent data. We consider independent tra
arXiv:2609.31665v1 Announce Type: new Abstract: Action anticipation predicts future human actions from partial video under incomplete context and temporal uncertainty. Recent systems introduce large l
arXiv:2605.06332v2 Announce Type: replace Abstract: Constructive neural routing solvers usually score the next action by matching a decoder context to candidate embeddings, leaving deterministic one-s
arXiv:2502.13440v2 Announce Type: replace-cross Abstract: Prevailing bioacoustic classifiers assign species labels to fixed time-frequency windows rather than to individual vocalizations. When multipl
arXiv:2609.34684v1 Announce Type: cross Abstract: Accurately decoding object states from the internal representations of vision-language-action (VLA) models does not establish that the predictions res
arXiv:2609.33760v1 Announce Type: new Abstract: We consider contextual binary prediction with i.i.d. contexts from an unknown distribution and adaptively chosen losses. We show that a simple Follow-th
arXiv:2609.32259v1 Announce Type: cross Abstract: Recent multi-agent LLM systems increasingly combine heterogeneous models for specialized agent roles. However, text-based communication requires each
arXiv:2609.34562v1 Announce Type: new Abstract: MeMo (Zanzotto et al., 2025) is a recent language-model architecture that stores associations between token contexts and next tokens in a correlation ma
arXiv:2609.31358v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) provides a common interface through which AI applications discover and use external resources and tools. It allows la
arXiv:2609.31093v1 Announce Type: new Abstract: Scaling language models to long contexts is limited by the quadratic cost of self-attention. Block sparse attention offers an efficient alternative, but
arXiv:2609.31568v1 Announce Type: new Abstract: AI tutoring could markedly improve learning outcomes for students in developing regions such as Vietnam, yet the two obvious paths both fall short. Clou
Some context first. I am process improvement / business consultant and had worked with Fortune 500 companies on improving their processes around refunds, returns, customer support, etc. This entire th
arXiv:2609.30662v1 Announce Type: new Abstract: Large language models (LLMs) can plan, use tools, write code, and execute long-horizon workflows, yet strong local competence does not guarantee project
retrieval is a decision problem, not just a similarity problem cool experiment from @assaf_elovic swapping embeddings for Jev in GPT Researcher - 73% vs 46% relevant context decision models will show
Hey guys! I have been excited to share this here. This is a project consisting of kernel optimizations for the Tesla p100 series graphics card ($80). I want to start by saying I am 17 years old and do
Early this year when I was first looking at building up my inference capability you could get the 16GB Tesla P100s for between 60 and 80. Asked claude about it, told me absolutely not worth it. No ten
🛠️ MiMo-V2.6 update: tool-call repetition, diagnosed & fixed. After the MiMo-V2.6 series models launched, we noticed them sometimes repeating identical or highly similar tool calls — burning context a
About a month ago I was having FOMO and was going to spend coin I don't really have on new graphics cards. Instead of doing that though I decided to spend the money on a new motherboard+cpu and try to
arXiv:2609.29773v1 Announce Type: new Abstract: Many real-world tasks (e.g., office workflows, scientific experimentation) require LLM agents to interact repeatedly with their environments for context
Compute (EC2) opened the front door to the first generation cloud. Agents need something different: compute, inference, and data working as one. We built the cloud that delivers all three as one simpl
arXiv:2609.30214v1 Announce Type: cross Abstract: We present Underwater C^{3}-JEPA (cross-view, control-conditioned, context-extended), an object-centric multi-view predictive world model for near-fie
Original post: https://www.reddit.com/r/LocalLLaMA/comments/1woscea/contrastive_language_models/ (sorry I felt it wasn't giving CLM the highlight it deserves) What it is: a new projection head for Qwe
arXiv:2609.28399v1 Announce Type: new Abstract: Language models typically construct attention values from contextual hidden states, even when some of their content may be reusable across contexts. We
Introduction The landscape of software supply chain security has undergone a significant shift. Recent campaigns demonstrate that sophisticated threat actors are systematically targeting the engineeri
arXiv:2609.27475v1 Announce Type: new Abstract: As robots remain in public spaces over extended periods, they must maintain interaction continuity by preserving and correctly applying context as peopl
arXiv:2609.27332v1 Announce Type: new Abstract: Long horizon agents accumulate growing interaction histories that increase context and inference costs. We find that geometric redundancy alone is an in
arXiv:2609.25053v1 Announce Type: new Abstract: Can one language model hand its live memory to another without the receiver rereading the context? We demonstrate useful persistent hybrid-state transfe
arXiv:2609.25506v1 Announce Type: new Abstract: Robot manipulation policies must generalize across visual shifts while preserving scene context relevant to action. General-purpose vision encoders are
arXiv:2609.23790v1 Announce Type: new Abstract: Every node in a multi-agent large language model (LLM) workflow retrieves context from memory and injects it into its prompt, where those injected token
arXiv:2609.22573v1 Announce Type: cross Abstract: LLM agents translate natural-language context, which may include attacker-controlled text, into privileged tool calls, so authorization must remain ef
Cool paper showing how effective tuning a system prompt for an agent can be. Recommended paper if you tune agent harnesses. This paper presents EvolveTrade, which treats a trading agent's system promp
Interesting work on long-horizon research agents. PrimeScientists can decide where a research agent spends its budget. Achieves 10.3% more reward with 50.6% fewer research attempts, under the same bud
arXiv:2609.23371v1 Announce Type: new Abstract: Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persistent i
arXiv:2609.23416v1 Announce Type: cross Abstract: Long-form audio performance is often summarized by context length and aggregate accuracy, obscuring how language, evidence, and task jointly shape dif
arXiv:2609.23286v1 Announce Type: new Abstract: 3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference contex
arXiv:2609.24372v1 Announce Type: new Abstract: In-context learning (ICL) based on large language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limit
# the What An engine to run Gemma 4 31B on blackwell under massive concurrency and rather specific workload patterns. I've been waiting for someone to do ninfer but for gemma, and, well, ended up havi