Week 23 — 2026-06-01 – 2026-06-07

This study demonstrates that synthetic deception in LLMs can be systematically analyzed through linear representations, revealing domain-invariant dishonesty patterns achievable via minimal fine-tuning.

PReMISE introduces a framework for discovering policy-level rubrics and auditing their reliability, preference fit, and robustness under LLM judges, with repair operations that improve judge accuracy and reduce exploitability.

COMPASS introduces a novel framework for aligning search agents with safety constraints by addressing retrieval-induced safety degradation through cognitive tree exploration and introspective alignment.

This paper introduces a novel approach to healthcare mechanism design by integrating program synthesis with strategic provider behavior modeling, enabling evaluation of mechanisms through equilibrium outcomes rather than fixed responses.

Introduces a persona-based evaluation framework for generative AI that captures cultural, demographic, and contextual variability through synthetic cognitive profiles, replacing monolithic benchmarks with pluralistic, perspective-dependent assessment.

This paper formally defines causal pathways for rare events and establishes testable implications that depend on causal abstractions rather than full system models.

Introduces context-dependent argumentation frameworks (CDAFs) and the ACTIVATION-MANIPULATION decision problem, extending Dung's theory to model strategic control over argument evaluation through context-specific defeat functions.

MAVEN introduces a lightweight symbolic reasoning framework to enhance generalization in agentic tool calling, demonstrating significant accuracy improvements on multi-step reasoning benchmarks without additional training.

AutoSci introduces a unified, memory-centric agentic system that automates the entire scientific research lifecycle, addressing gaps in existing systems by integrating structured memory, dynamic workflow execution, skill augmentation, and iterative evolution.

This paper disentangles two distinct capabilities in self-evolving LLM agents—harness-updating and harness-benefit—revealing that base model capability does not predict effectiveness in harness self-evolution.

Introduces AdaCoM, an external context management system that improves long-horizon task performance for frozen agents without retraining, revealing a fidelity-reliability trade-off in context management strategies.

COLLEAGUE.SKILL introduces an end-to-end system for distilling heterogeneous expert traces into inspectable, correctable AI skill packages grounded in human expertise and behavior.

GLIDE introduces an open-source Python library that unifies state-of-the-art prediction-powered inference (PPI) estimators and samplers for reliable evaluation of agentic systems, reducing reliance on costly human annotations or biased LLM judgments.

SCALE introduces a self-improving web agent framework that autonomously expands cognitive boundaries through adversarial roles and graph-based exploration, reducing reliance on handcrafted pipelines and expert trajectories.

HypoAgent introduces an agentic framework for interactive abductive hypothesis generation over knowledge graphs, addressing limitations in handling evolving dialogues and providing fine-grained diagnosis.

BilliardPhys-Bench introduces a synthetic benchmark to evaluate physical reasoning and visual dynamics prediction in multimodal large language models (MLLMs) through billiards scenarios.

This work identifies critical failure modes in shared-state collaboration for resource-constrained visual agents and introduces CoSee, a framework for diagnosing noise accumulation in multi-step reasoning.

This work introduces multiple SAT encodings for factored tasks and systematically evaluates their effectiveness, along with the impact of task transformations and parallelism on SAT-based planning performance.

QASM-Eval is the first comprehensive dataset designed to train and evaluate LLMs on OpenQASM-3 programs, focusing on hardware-facing features beyond quantum circuits.

FAM-Bench introduces the first multi-modal benchmark for evaluating condition-aware Food-as-Medicine reasoning, addressing the gap in health-aware decision-making in existing food AI benchmarks.

GraphARC introduces a scalable benchmark for abstract reasoning on graph-structured data, extending the ARC paradigm to evaluate generalization in graph transformations.

Unicorn introduces a scalable framework for high-dimensional time series forecasting by decoupling correlation modeling from channel identities, enabling cross-domain generalization and few-shot transfer.

This paper introduces novel map representations (Point-Line and Spatial-Layout) and demonstrates their superiority in generating diverse, high-quality FPS maps using MAP-Elites compared to existing representations.

Developed a deep learning benchmark (Gait2Hip-60) for predicting hip muscle forces and joint moments directly from gait kinematics, enabling faster and more clinically applicable alternatives to traditional musculoskeletal simulations.

LLM-FACETS introduces an open-source, privacy-preserving framework for evaluating LLM transparency and accountability, enabling non-technical practitioners to audit models without data transmission or programming expertise.

LinTree demonstrates that explicitly structuring search histories with parent pointers improves LLM reasoning performance and efficiency compared to implicit trace representations and heuristic-based search.

SLAT introduces a segment-level adaptive trimming framework to enhance the efficiency of chain-of-thought (CoT) reasoning in large language models by addressing structural redundancy without compromising accuracy.

UniScale unifies model routing and test-time scaling into a single optimization framework, overcoming limitations of decoupled approaches and achieving superior quality-cost trade-offs in dynamic inference scenarios.

PhyDrawGen addresses systematic errors in physics diagram generation by integrating symbolic physics constraints with visual generation, achieving robust physical accuracy across diverse domains.

This paper introduces a method for cross-model vector linking using local geometric consistency, enabling accurate correspondences between embedding clouds from different encoders without requiring model access or labels.

EHRBench introduces an automated, EHR-grounded benchmark for evaluating LLMs in clinical decision-making, addressing scalability and reliability gaps in real-world CDM tasks.

GRiD introduces a novel framework for discovering graph-like rules in knowledge graphs using diffusion models, addressing limitations of existing methods that overlook complex relational structures and computational challenges.

Introduces Feedback Distillation, a novel training method for reasoning models that improves post-training performance in complex tasks like Lean4 theorem proving by leveraging self-distillation with privileged feedback.

Proposes an uncertainty-aware framework that integrates expert advice with temporally regulated guidance to enhance safe and efficient exploration in reinforcement learning for autonomous driving.

DecomposeR introduces a planner-centric framework for deep research tasks using structured reward mechanisms, improving planning and answering capabilities in LLMs.

TraceGraph introduces a graph-based framework to analyze agent trajectories, revealing hidden navigation differences and motivating trap-aware recovery pipelines for improving model performance.

This paper introduces an Answer-Set Programming (ASP)-based implementation of the CARCASS framework for relational reinforcement learning, enabling efficient abstractions in complex domains using domain knowledge.

This paper introduces three rerooter designs for scalable subgoal-free policy tree search, enabling efficient search in complex environments without explicit subgoal generation.

This paper introduces a framework for physically viable world models in embodied AI that prioritize answering intervention queries through structural physical abstractions, rather than mere observation prediction.

This work introduces HADT, a novel transformer-based architecture for autonomous resource management in heterogeneous satellite clusters conducting Earth Observation missions, demonstrating significant performance improvements over existing baselines.