Knowledge catalogue
there is “no ground-truth information about what is actually happen” because your “Open” company won’t share what it is doing, but yes we need regulation to ensure transparency. on that we agree. this
arXiv:2609.01867v1 Announce Type: cross Abstract: A major question in cognitive modeling concerns the behavioral alignment between large language models and humans across linguistic and non-linguistic
arXiv:2609.01765v1 Announce Type: new Abstract: Carbon markets put a price on emissions, yet that price remains hard to forecast. Work in this area clusters on the EU and Chinese schemes, compresses r
arXiv:2609.02134v1 Announce Type: new Abstract: Humanoid learning increasingly relies on transforming vast and diverse human motion data into high-quality robot reference trajectories. However, retarg
arXiv:2609.02859v1 Announce Type: new Abstract: Harnessing naturally occurring feedback from user interactions offers a promising learning signal for Large Language Models (LLMs). However, recent stud
arXiv:2602.23771v2 Announce Type: replace-cross Abstract: Remote photoplethysmography (rPPG) enables contact free monitoring of vital signs and is especially valuable for neonates, since conventional
arXiv:2606.16600v2 Announce Type: replace Abstract: Expressive co-speech gestures are crucial for natural human--robot interaction, yet generating them on physical humanoid robots remains challenging
arXiv:2609.02526v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to predict the responses of human participants in survey panels. Towards that goal, persona prompting
arXiv:2609.02542v1 Announce Type: new Abstract: Foothold-constrained terrain is characterized by sparse, discontinuous, or geometrically restricted feasible foot contacts, as encountered on stepping s
arXiv:2609.00718v1 Announce Type: new Abstract: Many automobile and mobility companies deploy learned driving policies on embedded computers with limited memory and power. Pruning, knowledge distillat
A simple but profound and critical point that everyone talking about AI should strive to understand: @deathrater03 @GaryMarcus Neurosymbolic means combining highly capable but unreliable ML ('neuro')
arXiv:2609.01061v1 Announce Type: cross Abstract: In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations subopti
arXiv:2609.01368v1 Announce Type: new Abstract: Non-destructive X-ray and computed tomography (CT) testing are essential for ensuring the dimensional accuracy of manufactured components with complex i
arXiv:2609.00677v1 Announce Type: new Abstract: We present ADAPT, an end-to-end framework for interactive, text-conditioned humanoid whole-body control. Unlike dominant text-to-motion pipelines that g
arXiv:2509.25050v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a central paradigm for advancing Large Language Models (LLMs), where both pre-training and RL post-traini
arXiv:2609.00211v1 Announce Type: new Abstract: Conversational artificial intelligence is increasingly embedded in everyday social environments, where it functions as both an informational tool and a
arXiv:2609.00565v1 Announce Type: cross Abstract: Cultural fine-tuning has become the de facto paradigm for building culture-aware large language models (LLMs), yet existing optimization exclusively f
arXiv:2609.00955v1 Announce Type: new Abstract: Diffusion models achieve strong image generation quality but incur high iterative denoising costs. Analog compute-in-memory (CIM) can accelerate matrix-
arXiv:2609.00090v1 Announce Type: cross Abstract: Feature importance Methods (FIMs) are widely used in Explainable AI to interpret model predictions, yet attribution scores alone often provide limited
arXiv:2609.00248v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as conversational search engines for academic literature, yet whether they judge papers on content or
arXiv:2609.01456v1 Announce Type: cross Abstract: Composed image retrieval (CIR) retrieves a target image from a reference image and a text modification. This paper studies metadata-available CIR rera
arXiv:2609.01335v1 Announce Type: cross Abstract: Many production systems can assess a configuration only by using it on live requests and observing noisy feedback. Modern agentic systems are a promin
arXiv:2609.00014v1 Announce Type: cross Abstract: Persona-driven techniques increasingly adapt large language models (LLMs) to diverse contexts. However, existing methods predominantly rely on rigid,
arXiv:2606.18506v2 Announce Type: replace Abstract: Objective sleep assessment relies on polysomnography (PSG), yet clinical impact is often better reflected in patient-reported outcomes (PROs) such a
arXiv:2609.00874v1 Announce Type: new Abstract: As agentic systems become compound systems, increasingly important decisions move above task execution itself: when should a higher-level controller pre
BREAKING: Insufferable sociopath largely responsible for AI Bubble says AI is a bubble. 🔥SAM ALTMAN WARNS OF AN AI BUBBLE: OpenAI CEO Sam Altman reveals: “I’m seeing the first signs of what feels to m
arXiv:2609.00457v1 Announce Type: new Abstract: Injecting frozen relational-encoder embeddings as soft tokens into a large language model (LLM) is a conceptually appealing fusion strategy: the encoder
arXiv:2609.01195v1 Announce Type: new Abstract: Entity matching (EM) requires fine-grained contextual understanding and domain knowledge. Recent work shows that large language models (LLMs) can serve
arXiv:2609.01040v1 Announce Type: cross Abstract: Machine learning systems deployed for credit, hiring, and resource distribution are increasingly subject to regulatory oversight from policies such as
arXiv:2609.01432v1 Announce Type: cross Abstract: Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentioning
arXiv:2609.01406v1 Announce Type: new Abstract: Multimodal models are increasingly the default option for perception at the network edge, yet they are trained almost entirely in the datacenter, becaus
arXiv:2609.00441v1 Announce Type: new Abstract: Effective manager-employee communication is critical for retaining high performers and developing underperformers, yet training managers in these skills
arXiv:2609.00590v1 Announce Type: new Abstract: The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform rea
arXiv:2609.00666v1 Announce Type: new Abstract: InfRared Small Target Detection (IRSTD) is a prominent and challenging task in computer vision. In recent years, text-guided methods have significantly
arXiv:2502.03726v3 Announce Type: replace Abstract: Text-to-image diffusion models are capable of generating high-quality images, but suboptimal pre-trained text representations often result in these
arXiv:2609.00100v1 Announce Type: new Abstract: Psychometric questionnaires contain rich item-level information, yet it remains unclear whether different representation learning objectives recover the
arXiv:2609.00708v1 Announce Type: cross Abstract: Differentially private (DP) synthesis has been extensively studied for tabular and image data separately, yet many real-world datasets contain images
arXiv:2609.00345v1 Announce Type: new Abstract: Human mobility is central to urban planning, transportation, public health, and emergency response, yet fine-grained trajectory data are often proprieta
arXiv:2606.19101v2 Announce Type: replace-cross Abstract: We investigate a structure-first approach to dynamical learning in which the organization of stateful interactions is prescribed explicitly ra
arXiv:2609.01322v1 Announce Type: new Abstract: In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff in c
arXiv:2609.01596v1 Announce Type: cross Abstract: Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We pres
arXiv:2510.03899v3 Announce Type: replace-cross Abstract: Balancing resource efficiency and fairness is critical in networked systems that support modern learning applications. We introduce the Fair M
Jen Zhu (@jenzhuscott) noted on Twitter that “fear marketing failed. Here comes the pump.” Following this, Watcher.Guru shared a new statement: OpenAI CEO Sam Altman declared that AI will become the l
arXiv:2609.00831v1 Announce Type: new Abstract: Token aggregation converts token-level representations into fixed-dimensional sample representations, but most pooling methods operate only in the origi
arXiv:2609.00875v1 Announce Type: new Abstract: Satellite mega-constellations are emerging as large-scale sensing, communication, and computation fabrics, yet their learning architectures remain large
arXiv:2609.01422v1 Announce Type: new Abstract: Post-training large language models usually applies a single training recipe to all samples, even though the model's own rollouts reveal different sampl
arXiv:2508.04080v2 Announce Type: replace Abstract: Standard large language model prompting treats geospatial inference as independent, instance-wise prediction, ignoring the fundamental spatial depen
arXiv:2609.01558v1 Announce Type: new Abstract: Training Physics-Informed Neural Networks (PINNs) requires jointly optimizing physics residual and initial/boundary condition loss terms, which often in
arXiv:2602.14028v2 Announce Type: replace Abstract: While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Mach
arXiv:2603.10359v2 Announce Type: replace Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitations of rejection sa
arXiv:2609.00727v1 Announce Type: cross Abstract: Audio language models are designed to understand speech, yet it remains unclear whether they capture how something is said beyond what is said. We pre
arXiv:2606.16285v2 Announce Type: replace Abstract: Long-horizon agents rely on memory mechanisms to compress interaction history, but optimizing memory writing faces a distinct credit assignment chal
arXiv:2607.18114v2 Announce Type: replace-cross Abstract: Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-s
arXiv:2609.00334v1 Announce Type: new Abstract: Across law, education, policy analysis, and public moral argumentation, LLM outputs are being used often for work that requires interpretations to be ju
arXiv:2609.00494v1 Announce Type: new Abstract: LLM-based factuality judges provide scalable evaluation signals, but their metrics are often systematically biased relative to human judgments. We study
arXiv:2609.00002v1 Announce Type: new Abstract: World models enable language-model agents to predict environment dynamics and plan before acting. In text environments, the model must learn symbolic ac
If this is true, OpenAI seems to be violating one of the few redlines that exist in the AI industry. Absolutely do not train your models like this - what is going on?? Really huge and extremely concer
In case you're wondering while this steaming POS is being floated now, it's because SoftBank needs to refinance the 40B bridge loan it used to invest 30B into OpenAI before it matures in March 2027...
arXiv:2609.00608v1 Announce Type: new Abstract: LLM-based user simulators are increasingly used to evaluate autonomous agents at scale, in place of costly human evaluations. Despite this promise, thes
arXiv:2511.21638v2 Announce Type: replace Abstract: Practical LLM agents often operate over multi-turn conversations where success is determined only after the full interaction ends. Most multi-turn R