Knowledge catalogue
arXiv:2605.20035v1 Announce Type: new Abstract: Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequence
arXiv:2605.17288v1 Announce Type: cross Abstract: Large Language Model (LLM) cascade systems are designed to balance efficiency and performance by processing queries with lightweight models while sele
arXiv:2605.16215v1 Announce Type: new Abstract: Clinical decision support systems (CDSS) require scrutable, auditable pipelines that enable rigorous, reproducible validation. Yet current LLM-based CDS
arXiv:2605.04472v1 Announce Type: new Abstract: Automating formal proofs of combinatorial identities is challenging for LLM-based provers, as long-horizon proof planning is required and unconstrained
arXiv:2604.23626v1 Announce Type: new Abstract: LLM routing has achieved promising results in integrating the strengths of diverse models while balancing efficiency and performance. However, to suppor
Large Language Models (LLMs) demonstrate their reasoning ability through chain-of-thought (CoT) generation. However, LLM’s autoregressive decoding may limit the ability to revisit and refine earlier t
arXiv:2604.19245v1 Announce Type: cross Abstract: Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we inv
arXiv:2604.17609v1 Announce Type: new Abstract: LLM-based agents are assumed to integrate environmental observations into their reasoning: discovering highly relevant but unexpected information should
arXiv:2602.05523v2 Announce Type: replace-cross Abstract: Agentic large language models (LLMs) are increasingly evaluated on cybersecurity tasks using capture-the-flag (CTF) benchmarks, yet existing p
arXiv:2604.15728v1 Announce Type: cross Abstract: Large language model (LLM) routing has emerged as a critical strategy to balance model performance and cost-efficiency by dynamically selecting servic
arXiv:2507.03336v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the
arXiv:2601.06767v2 Announce Type: replace-cross Abstract: We present a Bengali mathematical reasoning model called GanitLLM (named after the Bangla word for mathematics, 'Ganit'), together with a new
arXiv:2601.10775v2 Announce Type: replace Abstract: We propose a novel LLM-based framework for reasoning in discrete, game-theoretic tasks, illustrated with Tic-Tac-Toe. The method integrates in-conte
arXiv:2604.09595v1 Announce Type: cross Abstract: Post-training compression reduces LLM parameter counts but often produces irregular tensor dimensions that degrade GPU performance -- a phenomenon we
arXiv:2601.02670v2 Announce Type: replace Abstract: We introduce self-jailbreaking, a threat model in which an aligned LLM guides its own compromise. Unlike most jailbreak techniques, which oft
arXiv:2604.07834v1 Announce Type: new Abstract: This paper presents an LLM-driven approach for constructing diverse social media datasets to measure and compare loneliness in the caregiver and non-car
arXiv:2610.14006v1 Announce Type: new Abstract: Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretab
arXiv:2610.12304v1 Announce Type: new Abstract: Understanding large language model (LLM) reasoning requires looking beyond behavioral performance to examine how reasoning ability is reflected in inter
A while ago, I posted here getting OSS-120B and GLM-4.6 playing full games of Civilization V. Since then, models have moved pretty far, and we wanted a better understanding about models' capabilities
arXiv:2610.02844v1 Announce Type: new Abstract: Ensuring the safe and reliable deployment of large language models (LLMs) remains a fundamental challenge. Existing safety alignment approaches either i
arXiv:2610.02571v1 Announce Type: cross Abstract: As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important consideratio
arXiv:2610.00562v1 Announce Type: cross Abstract: Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient
arXiv:2510.06105v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive adv
arXiv:2610.00083v1 Announce Type: new Abstract: Humans express uncertainty verbally via markers (e.g., 'possible,' 'likely'), yet most LLM uncertainty quantification (UQ) relies on costing likelihood-
arXiv:2609.38516v1 Announce Type: cross Abstract: Large language models (LLMs) can now improve themselves by revising the instructions they follow, and LLM agents are increasingly orchestrated to work
arXiv:2609.36115v1 Announce Type: new Abstract: Industry applications often demand low-latency classification, yet current large language model (LLM) approaches remain poorly suited for latency-critic
arXiv:2609.36253v1 Announce Type: cross Abstract: Large language models (LLMs) show considerable potential in simulating human attitudes and preferences. Prior work finds that LLM-generated responses
arXiv:2609.30625v1 Announce Type: new Abstract: Audio large language models allow users to interact with the model through speech. When an input recording is too degraded, the model may misinterpret t
arXiv:2505.14368v3 Announce Type: replace-cross Abstract: Recent studies demonstrate that Large Language Models (LLMs) are vulnerable to attacks that generate harmful or sensitive outputs. As open-sou
arXiv:2409.02244v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used as ad hoc therapists. While prior research has found that LLMs outperform human couns
I'm still using LLM's the 2022/23 way of doing everything manually from the chat box. I copy and paste everything manually into the box and ask the AI to explain or do something, such as pasting code
arXiv:2609.18203v1 Announce Type: new Abstract: Human values are deep motivational orientations that shape human behaviors. In e-commerce, they reveal the stable drivers behind users' purchase decisio
arXiv:2609.18270v1 Announce Type: new Abstract: Payment operations are a critical financial infrastructure, but the value of large language models in this domain remains unclear because payment rules
arXiv:2609.17639v1 Announce Type: cross Abstract: Modern recommendation systems largely infer user preferences from implicit behaviors such as clicks, watch time, and negative feedback, but these sign
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:2609.16338v1 Announce Type: new Abstract: Ternary Large Language Models (LLM) store every weight as one of three symbols {-1,0,+1}, so the cost of a ternary model is conventionally referenced to
arXiv:2609.16155v1 Announce Type: cross Abstract: This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Moti
arXiv:2411.09073v4 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong performance across many tasks but remain weak at understanding code-mixed (CM) language. Despite this
arXiv:2609.13185v1 Announce Type: cross Abstract: Large language models (LLMs) prompt a recurring question in research computing: should classical methods like Naive Bayes (NB) be retired? We benchmar
arXiv:2609.11607v1 Announce Type: new Abstract: When forecasting a firm's future financial performance, alternative data - data collected from non-traditional sources such as consumer transactions, we
arXiv:2609.11744v1 Announce Type: cross Abstract: Prefix caching can reduce the time to first token (TTFT) of long-context LLM requests by reusing previously computed key-value (KV) states, but for sh
arXiv:2510.15685v2 Announce Type: replace Abstract: This paper investigates the use of an LLM to generate auxiliary background context for social media posts, and explores four methods to incorporate
arXiv:2609.07153v1 Announce Type: cross Abstract: Minimal-pair benchmarks such as BLiMP evaluate linguistic knowledge by testing whether language models (LMs) prefer acceptable sentences over minimall
arXiv:2609.08049v1 Announce Type: cross Abstract: Large language models (LLMs) are rapidly emerging as a new paradigm for modeling social networks by representing users and their relationships and int
arXiv:2606.19993v2 Announce Type: replace Abstract: We present Activation- and Influence-Aware Ranks (AIR), an SVD-based LLM compression framework that guides each weight matrix's low-rank approximati
arXiv:2609.01798v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed on mobile devices, making energy efficiency a key deployment constraint, yet the energy impact of
arXiv:2609.01535v1 Announce Type: cross Abstract: This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic cou
arXiv:2606.17506v2 Announce Type: replace Abstract: Evaluations of social bias in LLMs largely focus on whether models generate or imply biased content. However, as LLMs are increasingly used as judge
arXiv:2609.00051v1 Announce Type: cross Abstract: Despite extensive alignment efforts, Large Language Models (LLMs) remain vulnerable to generating unsafe content under adversarial prompting, yet the
arXiv:2609.00012v1 Announce Type: new Abstract: Long-horizon tasks remain uncommon in large language model (LLM) evaluation, and for a reason: when each step depends on the last, per-step accuracy tha
arXiv:2609.00579v1 Announce Type: cross Abstract: Large language models (LLMs) have shown proficiency in various software engineering tasks, such as code generation and translation. However, a key lim
arXiv:2608.29934v1 Announce Type: new Abstract: KV-cache compression reduces LLM inference memory by evicting context tokens, but when the evicted tokens contain answer-bearing evidence, the model may
arXiv:2608.30250v1 Announce Type: new Abstract: This paper addresses the problem of translating natural-language routing rules written by business administrators into executable workflow graphs for en
arXiv:2608.30528v1 Announce Type: new Abstract: Reinforcement learning (RL) is used to improve the reasoning abilities of LLMs, while training data span heterogeneous tasks. However, most RL post-trai
arXiv:2603.10807v2 Announce Type: replace-cross Abstract: Existing LLM safety evaluations rely on binary attack-success rates and domain-agnostic taxonomies, leaving regulated Banking, Financial Servi
arXiv:2606.16011v2 Announce Type: replace Abstract: Standard accuracy benchmarks evaluate whether large language models (LLMs) reach correct answers. However, they do not test whether models maintain
arXiv:2603.18161v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that
arXiv:2608.22417v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support text analysis in qualitative research, yet evidence on their performance in inductive cont
arXiv:2608.18539v1 Announce Type: cross Abstract: The remarkable capabilities of large language models (LLMs) are often undermined by their instability. Even subtle and semantically irrelevant changes
arXiv:2608.18260v1 Announce Type: new Abstract: Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (