arXiv AI
Jun 16
Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models
arXiv:2606. 16902v1 Announce Type: cross Abstract: This work addresses spatial question answering for service robots traversing long egocentric routes.
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
Jun 16
Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering
arXiv:2606. 16890v1 Announce Type: cross Abstract: Aggregate accuracy benchmarks conceal a systematic structure in how large language models fail at electronic health record (EHR) question answering: questions requiring more inferential steps produce disproportionately more errors.
arXiv AI
Jun 16
Decoupling Semantics from Distortions: Multi-Scale Two-Stream Vision-Language Alignment for AI-Generated Image Quality Assessment
arXiv:2606. 16799v1 Announce Type: cross Abstract: Existing vision-language model (VLM)-based AI-generated image quality assessment (AIGIQA) methods suffer from a fundamental semantic-distortion dimensional conflict: monolithic representations optimized for semantic discrimination inherently entangle compositional understanding with low-level perceptual sensitivity, rendering them blind to fine-grained quality degradations.
arXiv AI
Jun 16
Sycophancy as Material Failure under Pushback Loading: A Multi-Axis Characterization Across Three Loading Cases and up to Seventeen Material Charges
arXiv:2606. 16617v1 Announce Type: cross Abstract: Sycophancy in LLMs is documented across 70+ papers, but expert agreement on construct boundaries remains low (ICC=.
arXiv Machine Learning
Jun 16
MIRAGE: Auditing Anti-Muslim Bias in Frontier LLMs Across Reasoning, Agentic, and Time-Coupled Conditions
arXiv:2606. 16562v1 Announce Type: new Abstract: Five years after the discovery of persistent anti-Muslim bias in large language models, most evaluations remain confined to single-turn prompt completion, a setting that no longer reflects how frontier LLMs are deployed.
arXiv AI
Jun 16
Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection
arXiv:2606. 16344v1 Announce Type: new Abstract: Travelers increasingly ask large language model (LLM) assistants which hotel to book, making these systems gatekeepers of property visibility -- yet what moves their recommendations is undocumented.
arXiv AI
Jun 16
SPARK: Security Knowledge Priming and Representation-Guided Knowledge Activation for LLM-based Secure Code Generation
arXiv:2606. 16244v1 Announce Type: cross Abstract: Large language models routinely generate code with exploitable security flaws.
arXiv AI
Jun 16
DeepRoot: A KG-Coordinated Multi-Agent System for Therapeutic Reasoning over Historical Medical Texts
arXiv:2606. 15931v1 Announce Type: cross Abstract: Historical medical archives and traditional medicines hold immense potential for drug discovery and remain a primary source for current drug development.
arXiv AI
Jun 16
Data Augmentations for Data-Constrained Language Model Pretraining
arXiv:2606. 16246v1 Announce Type: cross Abstract: As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shifting toward a data-constrained, compute-abundant regime that demands productive multi-epoch training on fixed corpora.
arXiv AI
Jun 16
Tensor-Coord: Algebraic Decomposition of Joint Plan Tensors for Conflict-Free Multi-Agent LLM Planning
arXiv:2606. 16478v1 Announce Type: new Abstract: Large language models (LLMs) remain limited in multi-agent planning because independently generated plans can create coordination failures such as spatial collisions, resource contention, and temporal deadlocks.
arXiv AI
Jun 16
RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs
arXiv:2602. 05367v3 Announce Type: replace Abstract: Efficient deployment of large language models (LLMs) requires extreme quantization, forcing a critical trade-off between low-bit efficiency and performance.
arXiv AI
Jun 16
PreLort: Prefix-Nested LoRA for Federated Fine-Tuning under Rank Heterogeneity
arXiv:2606. 15963v1 Announce Type: cross Abstract: Federated fine-tuning of large language models using parameter-efficient methods such as LoRA enables privacy-preserving adaptation of foundation models.
arXiv AI
Jun 16
Architectural Wisdom: A Framework for Governing Optimization in AI Systems
arXiv:2606. 16319v1 Announce Type: new Abstract: Modern AI systems exhibit structural failures that capability scaling alone does not reliably fix: they optimize under-specified objectives with no architectural mechanism to question whether the objective should be optimized at all.
arXiv AI
Jun 16
When in Doubt, Plan It Out: Committed Small Language Model Deliberation for Reactive Reinforcement Learning
arXiv:2606. 16995v1 Announce Type: new Abstract: Reinforcement Learning (RL) policies often degrade in unfamiliar environments because they lack explicit deliberation.
arXiv AI
Jun 16
Orchestrated Reality: From Role-Play to Living, Playable Game Worlds -- LLM-Driven World Simulation as a Parameterized-Action POMDP
arXiv:2606. 16014v1 Announce Type: cross Abstract: Many games rely on storytelling combined with systems that track levelling, NPC behaviour, and consequence simulation; bridging tightly-authored narrative with deeply-simulated worlds -- most acute in sandbox and open-world settings -- has been prohibitively expensive.
arXiv AI
Jun 16
CONCORD: Asynchronous Sparse Aggregation for Device-Cloud RAG under Document Isolation
arXiv:2606. 15179v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has emerged as a pivotal technique for improving language models by incorporating external knowledge at inference time.
arXiv AI
Jun 16
Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning
arXiv:2606. 15231v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios.
arXiv Machine Learning
Jun 16
A Mechanistic Understanding of Pronoun Fidelity in LLMs
arXiv:2606. 16407v1 Announce Type: cross Abstract: Faithful and robust pronoun use is important for fair and coherent generations, yet large language models largely fail when multiple referents use different pronouns.
arXiv Machine Learning
Jun 16
Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense
arXiv:2605. 30837v2 Announce Type: replace-cross Abstract: Prompt-injection detectors are heterogeneous: each is strong on a different slice of attacks, and none is always reliable.
arXiv AI
Jun 16
PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty
arXiv:2606. 15654v1 Announce Type: cross Abstract: Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive.