Knowledge catalogue
arXiv:2609.09764v1 Announce Type: new Abstract: Social intelligence enables agents to read social context, infer intent, and adapt over sustained dialogue. As language models become autonomous collabo
arXiv:2609.05928v1 Announce Type: cross Abstract: Large language models now compute correct tax liabilities on over 90% of well-formed cases in statutory benchmarks, which makes them candidates for th
arXiv:2609.08003v1 Announce Type: new Abstract: Behavioral foundation models have been proposed as stand-ins for human participants across settings, but it is unclear whether theories discovered on th
arXiv:2609.09159v1 Announce Type: new Abstract: The topological gap Delta -- the excess H_1 total persistence of a critical point cloud over a density-matched null -- scales as Delta sim L^{d+eta}. We
arXiv:2609.05800v1 Announce Type: new Abstract: Activation steering controls LLM behavior at inference time by adding learned directions to hidden states, but existing methods handle one concept at a
arXiv:2609.10356v1 Announce Type: new Abstract: Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban in
arXiv:2609.08950v1 Announce Type: cross Abstract: Text-to-SQL systems translate natural language queries into executable SQL, democratizing access to structured data. Despite recent advances driven by
arXiv:2609.06540v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical applications, yet jailbreak attacks can conceal harmful intent through role-
arXiv:2609.08452v1 Announce Type: new Abstract: Multi-agent large language models solve complex tasks by coordinating several policies in a shared environment. However, existing reinforcement learning
arXiv:2609.06961v1 Announce Type: new Abstract: Existing car-following prediction methods mainly optimize trajectory accuracy, while rarely considering whether predicted disturbances propagate realist
arXiv:2609.09989v1 Announce Type: new Abstract: A natural way to cut reasoning-model inference cost is to repeatedly probe a single partial trajectory for its current answer and stop once probes agree
arXiv:2609.07344v1 Announce Type: cross Abstract: Large language model (LLM) based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration test
arXiv:2609.09247v1 Announce Type: cross Abstract: Local heatwave hazard depends on fine-scale air temperature, but ground stations are sparse and reanalysis products such as ERA5 cannot resolve the te
arXiv:2609.09264v1 Announce Type: new Abstract: Leading benchmarks for formal theorem proving with large language models are small collections drawn from competition math, such as the IMO and Putnam,
arXiv:2609.10112v1 Announce Type: new Abstract: Existing knowledge-base-assisted semantic communication schemes commonly adopt either single knowledge-base quantization (SKBQ) or multi-knowledge-base
arXiv:2609.09899v1 Announce Type: cross Abstract: Language models can fluently describe how they would behave: whether they would cave to pushback, misuse a tool, or lie under pressure. Is that descri
arXiv:2609.09719v1 Announce Type: new Abstract: Text-aligned speech tokenization methods have emerged to better align speech tokens with LLM token spaces, enabling more effective utilization of pretra
arXiv:2609.09890v1 Announce Type: new Abstract: Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple ou
arXiv:2609.10276v1 Announce Type: cross Abstract: This paper introduces an evolutionary computation approach for consensus in structural Bayesian Network (BN) fusion under the constraint of limited tr
arXiv:2609.10017v1 Announce Type: new Abstract: Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements ar
arXiv:2609.08236v1 Announce Type: new Abstract: Automatic safety judges -- systems such as Llama Guard or a GPT-4o grading prompt that decide whether a model's reply is harmful -- produce the numbers
arXiv:2609.09233v1 Announce Type: cross Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused o
arXiv:2609.06128v1 Announce Type: new Abstract: Production LLM agents execute tool-calling loops, retrieval chains, and compositional workflows in multiple modes, yet execution semantics are often cou
arXiv:2609.09295v1 Announce Type: cross Abstract: The fixed-charge network flow problem (FCNFP) couples continuous flow allocation with discrete arc-activation decisions, making it a canonical but com
arXiv:2609.08149v1 Announce Type: new Abstract: SWE-Bench Pro has emerged as a standard benchmark for evaluating software engineering agents on challenging repository-level tasks. However, our analysi
arXiv:2609.06229v1 Announce Type: cross Abstract: Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical s
arXiv:2609.10283v1 Announce Type: new Abstract: Brachiation enables primates to move across overhead supports when ground paths are blocked, suggesting a complementary locomotion mode for robots opera
arXiv:2609.09349v1 Announce Type: new Abstract: Modern LLMs demonstrate impressive multilingual performance, yet standard benchmarks primarily reward selecting correct answers rather than evaluating g
arXiv:2606.16893v2 Announce Type: replace Abstract: Symbolic informalization enables a reliable conversion of formal mathematics to natural language. It has the potential to make machine-checked conte
arXiv:2609.09772v1 Announce Type: new Abstract: SymbolicLight V2 combines sparse event computation with continuous-state processing in a hybrid neuromorphic language architecture. Extending V1's spike
arXiv:2505.03380v2 Announce Type: replace Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains
arXiv:2609.10303v1 Announce Type: new Abstract: Face recognition (FR) is a widely used modality for biometric authentication, but conventional models rely on visible-spectrum imagery and degrade when
arXiv:2606.15405v2 Announce Type: replace-cross Abstract: Long-term memory is essential for conversational agents to remain coherent across extended dialogues, follow through on commitments made many
arXiv:2601.19275v2 Announce Type: replace-cross Abstract: Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncer
arXiv:2502.08221v2 Announce Type: replace Abstract: The growing demand for efficient semantic communication systems capable of managing diverse tasks and adapting to fluctuating channel conditions has
arXiv:2609.05631v1 Announce Type: cross Abstract: A voice agent has to decide, at every pause, whether the user has finished speaking. Without a model of the language that decision falls back to a fix
arXiv:2609.09595v1 Announce Type: new Abstract: Teacher-student systems, in which a teacher neural network generates training labels so that a student neural network can learn to implement the same fu
arXiv:2609.09552v1 Announce Type: new Abstract: In this paper, we solve two fundamental obstacles in applying LLMs to critical domains: token efficiency and faithfulness. To address both constraints j
arXiv:2506.03889v3 Announce Type: replace Abstract: When training autoregressive models to forecast dynamical systems, a critical question arises: how far into the future should the model be trained t
arXiv:2609.09840v1 Announce Type: new Abstract: Accurate long-term trajectory prediction for maritime vessels is essential for safety and logistical efficiency. While deep learning models, particularl
arXiv:2609.09258v1 Announce Type: cross Abstract: We present a method for recovering the moral graph of a causal DAG from a probability distribution over discrete variables, using fully connected tens
arXiv:2609.09434v1 Announce Type: new Abstract: In recent years, weak-form methods have made significant advances in data-driven discovery of dynamical systems. However, in high-dimensional settings,
arXiv:2507.22076v2 Announce Type: replace Abstract: Text-to-image (T2I) generation models have made significant strides but still struggle with prompt sensitivity: even minor changes in prompt wording
arXiv:2609.10237v1 Announce Type: new Abstract: A graph retrieval-augmented generation pipeline chooses which triples to put in the prompt, a syntax to write them in, an order to write them in, and a
arXiv:2609.09048v1 Announce Type: cross Abstract: Whether a language model looks demographically biased can depend on how the audit asks its question. A charitable-aid benchmark reports that the same
arXiv:2609.05643v1 Announce Type: new Abstract: This Comment emerges from TPC26 (https://tpc26.org), a conference convening leaders from academia, national laboratories, and industry who are reshaping
arXiv:2509.24877v3 Announce Type: replace Abstract: The social science of large language models (LLMs) examines how these systems evoke mind attributions, interact with one another, and transform huma
arXiv:2609.07713v1 Announce Type: new Abstract: Generative and agentic AI are reshaping both the production and evaluation of scientific research. These developments are often studied separately, as q
arXiv:2609.05894v1 Announce Type: new Abstract: The exponentiation of Artificial intelligence (AI) in the recent past has entered a transformative era that has been driven by the growth in large langu
arXiv:2609.05514v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents are increasingly used as proxies for human participants in social science research, yet it remains unclear wheth
arXiv:2510.08831v2 Announce Type: replace Abstract: As AI writing tools become widespread, we need to understand how both humans and machines evaluate literary style, a domain where objective standard
arXiv:2609.07478v1 Announce Type: new Abstract: Large language models act as strategic agents and models of human choice, yet choosing like a strategic agent does not mean computing like one. We recor
arXiv:2609.09368v1 Announce Type: new Abstract: We present the Living Library, an end-to-end framework for transforming fragmented digital archives into governed, conversational, in-person exhibit exp
arXiv:2609.09496v1 Announce Type: new Abstract: Algorithmic outputs now populate the digital environments through which contemporary life is organized. The role of law in facilitating and constituting
arXiv:2609.05749v1 Announce Type: new Abstract: Work on the risks of artificial intelligence has focused predominantly on capability risk: the danger that systems become too powerful, too autonomous,
arXiv:2609.07731v1 Announce Type: new Abstract: We show that ordinary business language --- 'maximize profitability' --- induces profit-oriented ambiguity resolution: LLMs systematically dismiss ambig
arXiv:2609.06064v1 Announce Type: cross Abstract: Stochastic min-max optimization has attracted increasing attention due to its applications in modern machine learning, while existing theoretical stud
arXiv:2609.10141v1 Announce Type: cross Abstract: How many past requests are needed to decide which qubits should share entanglement? We show that the answer depends on the allocation choices created
arXiv:2609.10296v1 Announce Type: new Abstract: Non-invasive speech decoding remains constrained by the low signal-to-noise ratio of neural recordings, which makes fine-grained reconstruction of phone
arXiv:2609.09094v1 Announce Type: new Abstract: Combining search with function approximation has driven major advances in game-playing programs, making self-play algorithms more competitive than ever.