arXiv AI
Jul 28
Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models
arXiv:2607. 23976v1 Announce Type: cross Abstract: Appending a two-word confirmation tag to a decision question -- "Is X the better choice?
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jul 28
LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning
arXiv:2607. 22777v1 Announce Type: cross Abstract: Protein language models learn transferable sequence representations.
arXiv AI
Jul 28
Beyond Direct Answering: Aligning Educational LLMs as Socratic Guides via Heuristic Reinforcement Learning
arXiv:2607. 22996v1 Announce Type: cross Abstract: Large language models (LLMs) deployed in educational settings often behave as direct answerers: they disclose target concepts in the opening turn instead of guiding students through progressive inquiry, as Socratic pedagogy prescribes.
arXiv AI
Jul 28
Spatial Reasoning in LLM Game Agents: Impact of Causal Context and Multi-Step Planning
arXiv:2607. 22732v1 Announce Type: new Abstract: LLM-based game agents often perform poorly on more complex tasks.
arXiv AI
Jul 28
TopoFE: topology-aware LLM-guided Automated Feature Engineering
arXiv:2607. 23286v1 Announce Type: new Abstract: Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program synthesis problem, where the objective is to discover predictive feature transformations from an exponentially large search space.
arXiv AI
Jul 28
LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish
arXiv:2510. 07074v2 Announce Type: replace-cross Abstract: Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts.
arXiv AI
Jul 28
MM-ShiftKV: Decode-Aware Prefill-Stage KV Selection for Multimodal Large Language Models
arXiv:2607. 22586v1 Announce Type: new Abstract: Key-Value (KV) caching is essential for efficient inference in multimodal large language models (MLLMs), yet its memory footprint grows linearly with context length and becomes a major bottleneck due to the large number of visual tokens.
arXiv AI
Jul 28
Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning
arXiv:2607. 22621v1 Announce Type: new Abstract: While large language models (LLMs) enable strong question answering (QA), budgeted deployment is complicated by nondeterminism and heterogeneous resource profiles (cost, latency, and energy).
arXiv AI
Jul 28
Training Language Models to Cooperate with Inference-Time Controllers
arXiv:2607. 23771v1 Announce Type: new Abstract: Large language model (LLM) performance increasingly depends not only on the base model, but also on the inference-time controller used to organize reasoning.
arXiv AI
Jul 28
Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study)
arXiv:2607. 22880v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have driven growing interest in using LLMs to automate test generation.
arXiv AI
Jul 28
A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning
arXiv:2601. 09624v2 Announce Type: replace-cross Abstract: Machine unlearning is becoming essential for building trustworthy and compliant language models.
arXiv AI
Jul 28
Reasoning or Memorization: Can LLMs Understand and Generate Chinese Xiehouyu Riddles?
arXiv:2607. 23440v1 Announce Type: cross Abstract: In this paper, we push the boundary of LLM reasoning by testing them in a Chinese language game, xiehouyu, with novel xiehouyu created by linguists that had not existed before to avoid data contamination.
arXiv Machine Learning
Jul 28
WorldDiT: A Unified Diffusion Architecture for World and Action Modeling
arXiv:2607. 23909v1 Announce Type: new Abstract: Many recent robot policies pursue stronger control by using large pretrained vision-language models (VLMs) as the action backbone.
arXiv AI
Jul 28
Mission-Level Runtime Assurance for LLM-Assisted ISR Swarms over a Verification-Aware Fabric
arXiv:2607. 23532v1 Announce Type: cross Abstract: Swarms of LLM-assisted autonomous robots are increasingly proposed for cooperative intelligence, surveillance, and reconnaissance (ISR) in contested environments.
arXiv AI
Jul 28
Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
arXiv:2607. 22883v1 Announce Type: cross Abstract: While Large Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests can be negatively impacted when models are prompted with buggy code.
arXiv AI
Jul 28
The Lattice Representation Hypothesis of Large Language Models
arXiv:2603. 01227v3 Announce Type: replace Abstract: We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical operations in embedding geometry.
arXiv Machine Learning
Jul 28
MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model
arXiv:2607. 23607v1 Announce Type: new Abstract: Molecular structure elucidation from tandem mass spectra (MS/MS) is a central inverse problem in analytical chemistry.
arXiv Machine Learning
Jul 28
Repeated-Token Counting Reveals a Dissociation Between Representations and Outputs
arXiv:2605. 09239v2 Announce Type: replace-cross Abstract: Large language models fail at counting how many times a word repeats in a list, even though they perform well on far harder reasoning tasks.
arXiv AI
Jul 28
ATLAS: Automated Approximation of Transformers for Efficient Homomorphic Inference in One Hour
arXiv:2607. 23478v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) provides strong cryptographic guarantees for private inference, but deploying transformer models under FHE remains prohibitively expensive.
arXiv AI
Jul 28
Voice-Driven Semantic Perception for UAV-Assisted Emergency Networks
arXiv:2602. 17394v2 Announce Type: replace-cross Abstract: Unmanned Aerial Vehicle (UAV)-assisted networks are increasingly foreseen as a promising approach for emergency response, providing rapid, flexible, and resilient communications in environments where terrestrial infrastructure is degraded or unavailable.