Knowledge catalogue
arXiv:2610.10409v1 Announce Type: cross Abstract: General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carr
arXiv:2610.08513v1 Announce Type: cross Abstract: LLMs are increasingly deployed as autonomous agents in social environments, making it critical to study their ability to faithfully simulate human int
arXiv:2610.06102v1 Announce Type: new Abstract: We investigate CLIP for zero-shot gender estimation from full-face and periocular images. Three CLIP backbones are evaluated on 11,299 frontal images fr
Purpose-built to stress-test computer vision models, depth cameras, and spatial AI against severe specular glare and geometric reflections. This 425-asset production archive features a custom faceted
Nonobench measures how well LLMs solve nonograms (picross). Each model gets the row and column clues once and returns the full grid. No tools, one attempt per puzzle. Method: - Standard mode: 30 puzzl
arXiv:2610.00833v1 Announce Type: cross Abstract: Fluent LLM explanations may not follow the evidence from a structured system. We present VERITYGATE, a four-gate checker for declared evidence IDs, en
arXiv:2609.39257v1 Announce Type: cross Abstract: Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In
arXiv:1806.11314v3 Announce Type: replace Abstract: Many works have been done on salient object detection using supervised or unsupervised approaches on colour images. Recently, a few studies demonstr
arXiv:2609.38460v1 Announce Type: new Abstract: Language agents must revise planned actions when evidence changes, permission is revoked, or a stop instruction arrives. A useful response is selective:
arXiv:2609.37853v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as social agents, yet credible human-like interaction requires more than fluent responses or pers
arXiv:2609.37647v1 Announce Type: cross Abstract: Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed o
arXiv:2609.35965v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require te
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:2609.29371v1 Announce Type: new Abstract: This paper presents BanglaTurn, a corpus for end-of-turn detection in Bangla conversational speech, and a model trained on it. The corpus holds 35,374 s
arXiv:2609.28607v1 Announce Type: new Abstract: Intermittent time series are common in spare-parts demand and retail sales. Since the cost of forecast errors is typically asymmetric, decisions such as
A while back I posted here about a CLI agent I'm building that parses tool calls out of a model's plain-text output instead of trusting native function-calling to work. Finally ran the actual numbers.
arXiv:2609.29292v1 Announce Type: new Abstract: In this work, we focus on photorealistic sign avatar modeling, which is crucial for effective communication with the Deaf community and is characterized
arXiv:2609.29580v1 Announce Type: new Abstract: A released table is often treated as an i.i.d. sample, although its repeated rows may encode business frequency, repeated entities, joins, resampling, o
With the release of ThinkingCap-Qwen3.8-27B, I thought it would be worthwhile to do a comparison between the original Qwen3.8-27B, the new ThinkingCap, and Swift-Qwen3.8-27B. Both Swift which I alread
arXiv:2608.28021v2 Announce Type: replace-cross Abstract: Large language models increasingly author Infrastructure-as-Code (IaC), where one insecure default is provisioned straight into production. Pr
arXiv:2609.26502v1 Announce Type: cross Abstract: Deep generative models are widely reported to enable de novo crystal structure prediction (CSP), but their capability has not been measured consistent
arXiv:2609.26578v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced CT remains clinically challenging because clear cell R
arXiv:2609.22583v1 Announce Type: new Abstract: Predictive maintenance in Industry 4.0 refers to using data from sensors, machines, and production systems to estimate when equipment is likely to fail,
arXiv:2609.24190v1 Announce Type: new Abstract: Artificial intelligence (AI) has advanced at a rapid pace in recent years. Initially, breakthroughs in large language models caught widespread attention
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
On 22 September 2026, Allie K. Miller posted on Twitter that the Opus 5.5 model led her to curse at Claude less frequently. She notes that while reducing profanity toward another AI is an undesirable
arXiv:2609.24064v1 Announce Type: new Abstract: New Guinea is the world's richest island flora (~2,856 orchid species), yet most species are represented by only a handful of photographs, far fewer tha
arXiv:2609.21490v1 Announce Type: new Abstract: Gender bias remains a persistent concern in machine translation (MT), affecting both generated translations and their automatic evaluation. When a sourc
arXiv:2609.21909v1 Announce Type: new Abstract: In this work, we conduct a systematic comparison of two state-of-the-art motion-imitation reinforcement learning (MIRL) pipelines, one built on SCONE/Hy
arXiv:2609.19965v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest s
arXiv:2609.20218v1 Announce Type: cross Abstract: Large language models can label a tabular row from a plain-English description with no training - a capability now shipping in mainstream spreadsheet
arXiv:2609.18130v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) are effective methods for solving expensive optimization problems (EOPs), where surrogate models re
arXiv:2609.18406v1 Announce Type: new Abstract: Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prosth
arXiv:2609.16199v1 Announce Type: cross Abstract: This article describes an open image dataset for developing and evaluating active-fire segmentation methods in satellite imagery. The dataset contains
arXiv:2609.16777v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly serve as primary knowledge retrieval interfaces, their robustness against extit{persuasion attacks}---attem
arXiv:2609.16672v1 Announce Type: new Abstract: Background and Objective: Colonoscopy recording practice preserves text reports and still photographs, while the spatial information already present in
Suppose I create two machine learning models suppose tree and neural network for a task let's suppose regression problem, now suppose I am sending both of this paper to two different journals, now the
arXiv:2609.13072v1 Announce Type: cross Abstract: Fine-grained control of language model behaviors (e.g., steering) is among the more actionable outcomes of interpretability research. For binary conce
arXiv:2609.10801v1 Announce Type: new Abstract: Velocity-aware pitch control is standard, but under a broadcast viewport half the players are off screen and on-screen velocities come from a drifting c
I kept picking local models badly. Parameter count, quantization, VRAM. I could read all of it and still not know whether a model would be any good on my machine. So I stopped guessing and wrote somet
How it works. The model being tested is given a server capable of running it's weights and full context. That server is placed in a median priced apartment. The AI is given a bank account with for ren
Was considering choosing a 4xRadeon AI Pro R9700 build for a office AI server. Curious to know what results you guys are getting? Mainly considering DeepSeek V4 flash and Qwen 3.8 Flash with offloadin
arXiv:2609.03734v1 Announce Type: cross Abstract: BLEU-4 is the standard metric for evaluating sign language translation (SLT), but spoken-language metrics may not adequately reflect sign language pro
arXiv:2608.29286v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructio
arXiv:2608.28182v1 Announce Type: cross Abstract: Populations of large language model agents are increasingly used as experimental societies. Three doubts shadow every such result: whether the agents
arXiv:2608.26224v1 Announce Type: cross Abstract: Event triggering sits at the heart of high-energy physics, where the rare events of interest must be retained while an overwhelming background is disc
arXiv:2608.24934v1 Announce Type: new Abstract: Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Mul
Hi all! I've been building a local agent harness, but before writing the code, I wanted to get some intuition around whether (and how) structuring agent execution as a graph helps vs. standard tool-ca
arXiv:2601.17027v2 Announce Type: replace-cross Abstract: While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by t
arXiv:2608.23763v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has emerged as the standard layer connecting Large Language Model agents to external tool backends. This openness int
arXiv:2508.17527v2 Announce Type: replace Abstract: Accurately predicting travel mode choice is essential for effective transportation planning, yet traditional statistical and machine learning models
arXiv:2608.23095v1 Announce Type: new Abstract: Media bias detection relies on definitions and examples that specify what counts as bias, yet these specifications often vary across datasets or remain
arXiv:2608.21488v1 Announce Type: cross Abstract: While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exp
arXiv:2608.21775v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications, yet they remain vulnerable to generating harmful content. From advers
arXiv:2608.22331v1 Announce Type: new Abstract: We audit measurement variability for 3 native tool-calling endpoints across 2 providers on the official BFCL multiple and parallel categories, using mat
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
arXiv:2608.20362v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is a standard recipe for training large language models on mathematical reasoning, where an answer
arXiv:2608.21360v1 Announce Type: new Abstract: Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and gui
My hobby project skill-issue is an arena FPS in a single C file, about 34k lines, everything procedural. My test for local setups is to have an agent port it to a single file HTML version with three.j
https://omlx.ai/benchmarks/performance/2pko3m1k - you can expand the raw args, but I have full annotations of what worked and what didn't. I'm now testing the model on acutal coding and haven't seen a