Knowledge catalogue
arXiv:2510.27544v2 Announce Type: replace Abstract: Temporal reasoning involves understanding how systems evolve over time through input-driven state transitions. A key aspect is temporal causal reaso
arXiv:2606.07462v1 Announce Type: new Abstract: As foundation models advance and agent scaffolding becomes increasingly sophisticated, agents have demonstrated remarkable proficiency in complex, long-
arXiv:2510.26714v5 Announce Type: replace-cross Abstract: Machine unlearning aims to remove the influence of certain data points from a trained model without costly retraining. Most practical unlearni
arXiv:2606.07441v1 Announce Type: new Abstract: Sycophancy in language models is typically studied as excessive agreement or validation, while explicit praise and flattery have received comparatively
arXiv:2606.06920v1 Announce Type: cross Abstract: Deploying Small Language Models (SLMs) on edge devices requires efficient fine-tuning strategies that adapt models to new tasks without degrading thei
arXiv:2606.06212v1 Announce Type: new Abstract: Misconfigurations in computer networks remain a major source of critical Internet outages. Research is turning to Large Language Models (LLMs) to automa
arXiv:2504.10823v4 Announce Type: replace Abstract: Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been li
arXiv:2606.04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows. These evaluations fundamentally fail to measure a critical next
arXiv:2606.04906v1 Announce Type: cross Abstract: Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detectio
arXiv:2602.07842v2 Announce Type: replace Abstract: Confidence calibration is essential for making large language models (LLMs) reliable, yet existing training-free methods have been primarily studied
arXiv:2606.03334v1 Announce Type: new Abstract: Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all
arXiv:2511.20615v2 Announce Type: replace-cross Abstract: This study aimed to explore the application of deep neural networks for whole-body human posture prediction during dynamic load-reaching activ
arXiv:2604.17621v2 Announce Type: replace Abstract: Many real-world questions appear deceptively simple yet implicitly demand two capabilities: (i) systematic coverage of a bounded knowledge universe
arXiv:2605.29225v1 Announce Type: new Abstract: Self-evolving agents improve over time by reflecting on past failures, but existing evaluation is limited in two ways: it measures only task scores, lea
arXiv:2605.29341v1 Announce Type: cross Abstract: Multimodal large language models are increasingly deployed as long-horizon agents, where memory must do more than recall: it must track an evolving wo
arXiv:2605.24456v2 Announce Type: replace Abstract: Humans constantly reason about 3D proximity, the relations between their body and surrounding objects, to guide perception and action in daily life.
arXiv:2605.27315v1 Announce Type: new Abstract: Visual inputs are often assumed to improve language understanding in multimodal models. We examine this assumption by asking whether vision-language mod
arXiv:2605.22826v1 Announce Type: cross Abstract: Quantifying the deceptive potential of Large Language Models (LLMs) is critical for AI safety, yet difficult to achieve in uncontrolled environments.
arXiv:2602.11243v2 Announce Type: replace-cross Abstract: Modern LLM-based agents and chat assistants rely on long-term memory frameworks to store reusable knowledge, recall user preferences, and augm
arXiv:2605.18883v1 Announce Type: cross Abstract: A diffusion model trained on Hamiltonian trajectories can achieve rollout MSE near 10^{-3}, but the standard deviation of its energy over time is betw
arXiv:2605.16303v1 Announce Type: cross Abstract: Large language models (LLM) agents may offer tools to predict human responses to surveys. A common technique for defining these agents uses only demog
arXiv:2605.15511v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime n gg p, wher
arXiv:2601.16312v2 Announce Type: replace-cross Abstract: Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs are ineffective in this
arXiv:2605.13067v1 Announce Type: cross Abstract: As end-to-end robotic policies are progressively deployed in the real world to solve real tasks, they face a gap between the training and inference co
arXiv:2605.10075v1 Announce Type: new Abstract: Large language models (LLMs) require reliable evaluation from pre-training to test-time scaling, making evaluation a recurring rather than one-off cost.
arXiv:2602.13298v3 Announce Type: replace-cross Abstract: This paper investigates the relationship between convolutional neural network (CNN) topology and image recognition performance through a compa
arXiv:2605.02909v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (
arXiv:2507.01955v3 Announce Type: replace Abstract: Multimodal foundation models (MFMs), such as GPT-4o, have recently made remarkable progress. However, their detailed visual understanding beyond que
arXiv:2602.21720v2 Announce Type: replace-cross Abstract: Human recursive numeral systems (i.e., counting systems such as English base-10 numerals), like many other grammatical systems, are highly reg
arXiv:2604.23897v1 Announce Type: new Abstract: Markets are a promising way to coordinate AI agent activity for similar reasons to those used to justify markets more broadly. In order to effectively p
arXiv:2512.20298v2 Announce Type: replace-cross Abstract: Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. Thi
arXiv:2604.22207v1 Announce Type: cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate
arXiv:2512.22274v2 Announce Type: replace Abstract: We introduce GeCo, a geometry-grounded metric for jointly detecting geometric deformation and occlusion-inconsistency artifacts in static scenes. By
arXiv:2604.20577v1 Announce Type: cross Abstract: An assurance case is a structured argument document that justifies claims about a system's requirements or properties, which are supported by evidence
arXiv:2511.06168v3 Announce Type: replace Abstract: This paper primarily demonstrates a method to quantitatively assess the alignment between multi-step, structured reasoning in large language models
arXiv:2604.18728v1 Announce Type: cross Abstract: Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representatio
arXiv:2603.16120v2 Announce Type: replace Abstract: Deep Research (DR) systems help researchers cope with ballooning publishing counts. Such tools synthesize scientific papers to answer research queri
arXiv:2604.14619v1 Announce Type: cross Abstract: In computational paralinguistics, detecting cognitive load and deception from speech signals is a heavily researched domain. Recent efforts have attem
arXiv:2604.11721v1 Announce Type: cross Abstract: Governing common-pool resources requires agents to develop enduring strategies through cooperation and self-governance to avoid collective failure. Wh
arXiv:2604.09587v1 Announce Type: new Abstract: Mobile agents can autonomously complete user-assigned tasks through GUI interactions. However, existing mainstream evaluation benchmarks, such as Androi
arXiv:2604.08588v1 Announce Type: cross Abstract: Effective automation hinges on deciding when to act and when to escalate. We model this as a decision under uncertainty: an LLM forms a prediction, es
arXiv:2604.06942v1 Announce Type: cross Abstract: Ensuring ciphertext indistinguishability is fundamental to cryptographic security, but empirically validating this property in real implementations an
arXiv:2604.06403v1 Announce Type: cross Abstract: The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHab
arXiv:2603.15118v2 Announce Type: replace Abstract: We introduce VAREX (VARied-schema EXtraction), a benchmark for evaluating multimodal foundation models on structured data extraction from government
arXiv:2610.17702v1 Announce Type: new Abstract: Evaluating the quality of process models discovered from event logs is a fundamental challenge in process mining. Traditional evaluation relies on confo
arXiv:2610.17827v1 Announce Type: new Abstract: Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back to
arXiv:2610.20138v1 Announce Type: new Abstract: Batch black-box optimization is important for expensive systems where many candidates can be evaluated in parallel, yet only a few rounds of evaluation
arXiv:2608.28281v2 Announce Type: replace Abstract: Loop Engineering is emerging as a practice for organizing development work around coding agents. Instead of writing each prompt by hand, practitione
arXiv:2610.19823v1 Announce Type: new Abstract: Text-to-video (T2V) generation models can produce visually realistic videos that nevertheless violate the physical consequences implied by the prompt an
arXiv:2610.19011v1 Announce Type: new Abstract: The Practical Molecular Optimization (PMO) benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles
arXiv:2601.08689v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong capabilities in finance-oriented question answering, yet their evaluation on professional quantitativ
arXiv:2610.14720v1 Announce Type: new Abstract: World Action Models (WAMs) combine action generation with future prediction, allowing predicted futures to support action evaluation and robot decision
arXiv:2610.15452v1 Announce Type: new Abstract: Text-to-video (T2V) models are increasingly used to generate videos with targeted emotional expressions, yet existing evaluation methods mainly rely on
arXiv:2610.18826v1 Announce Type: cross Abstract: We introduce WorldOracle, a benchmark specifically designed to evaluate action-conditioned world models (ACWMs) against paired real-world executions.
arXiv:2610.12086v1 Announce Type: new Abstract: LLM-based program evolution relies on evaluation feedback to guide the iterative search for high-performing programs. However, evaluation is often compu
arXiv:2601.14994v2 Announce Type: replace-cross Abstract: Data contamination can invalidate benchmark evaluation when a model benefits from memorized evaluation content rather than genuine generalizat
arXiv:2610.02270v1 Announce Type: cross Abstract: Medical vision-language model (VLM) evaluation is sensitive to workflow design, prompting strategy, and benchmark construction, yet most studies treat
arXiv:2610.01162v1 Announce Type: new Abstract: Reliable video world models could provide scalable predictive environments for robot learning, planning, and evaluation. However, generated robot videos
arXiv:2609.39564v1 Announce Type: new Abstract: Delegating complete application development to coding agents requires preserving the intended design rather than simply producing plausible outputs thro
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