World models for reinforcement learning learn to simulate environment dynamics, yet what they represent internally remains unclear. This paper probes two architecturally distinct models—IRIS (a discrete token transformer) and DIAMOND (a continuous diffusion UNet)—on Atari Breakout and Pong using linear and MLP probes, causal interventions, and attention analysis to test whether they develop structured, interpretable representations of game state. The core finding is that world models develop approximately linear representations of salient state variables (ball position, score) that are not merely correlated but functionally used during prediction.

This paper tackles the persistent bottleneck of Multidisciplinary Software Development (MSD), where domain experts and software developers must manually coordinate across heterogeneous artifacts and incompatible formalisms. The authors model MSD workflows as a directed dependency graph $\mathcal{G}=(\mathcal{V},\mathcal{R})$ and propose an iterative optimization framework that replaces manual translation nodes with LLM-powered services. This matters because their approach reduces per-API development time from approximately 5 hours to under 7 minutes while maintaining production-quality code, demonstrating that workflow-level automation—not just coding assistance—can unlock substantial efficiency gains in industrial settings.

The paper addresses state-space explosion in partially observable environments by formalizing a bounded-interaction analogue of the Myhill-Nerode theorem for finite POMDPs. Its core insight is that two observation histories are equivalent if no bounded finite-state controller can distinguish them via closed-loop interaction, inducing a canonical quotient that is minimal and unique for that observer capacity. This yields a principled separation between exact decision sufficiency for observation-measurable objectives and approximate bounds for latent-state rewards, with the canonical object strictly requiring clock-aware probe families.

ARYA presents a world model architecture using "nano models"—small specialized components orchestrated by an autonomous agent (AARA)—rather than monolithic neural networks. The system claims physics-constrained determinism, sub-20-second training cycles, and an "unfireable" safety kernel that cannot be bypassed. The authors position this as production-deployed across seven industry domains from aerospace to pharma, achieving state-of-the-art results on six of nine benchmarks with "zero neural network parameters."

Unmanned aerial vehicle (UAV) path planning traditionally treats efficiency and safety objectives as a single multiobjective optimization problem. This paper proposes a biparty multiobjective formulation with separate decision-makers for efficiency and safety, adapting immune algorithms (NNIA, HEIA, AIMA) into BPNNIA, BPHEIA, and BPAIMA to find common Pareto optimal solutions. The work addresses the practical scenario where regulatory and operational departments have independent criteria.

Precision free-space optics demands sub-millimeter and sub-degree tolerances where traditional robotic pick-and-place fails. This work introduces a closed-loop robotics framework integrating hierarchical computer vision, Newton-based spatial optimization, and Bayesian angular optimization to autonomously construct, align, and maintain optical systems. The authors demonstrate this by building a tabletop laser cavity from randomly distributed components—achieving beam alignment, mode selection, and self-recovery without human intervention. The system bridges the gap between coarse robotic manipulation and the extreme precision required for functional optical experiments.

The paper tackles the challenge of controlling high-level behavioral traits in LLM agents deployed in strategic settings. Rather than treating models as black boxes via prompting, the authors construct 'persona vectors'—linear directions in activation space—for traits like altruism and forgiveness using contrastive activation addition. Applied to six canonical games, these vectors allow both measurement of behavioral tendencies and causal steering of decisions, offering a mechanistic handle on strategic behavior.

Organizations deploying commercial AI systems inherit vendor-imposed value constraints that limit which recommendations the system can produce. This paper formalizes these boundaries as a "behavioural feasible set" and demonstrates through controlled experiments that alignment training compresses this set, making AI systems structurally unable to endorse certain legitimate organizational actions even under strong contextual pressure. The work reframes AI governance from a capability question to a constraint diagnosis problem, showing that vendor selection partially determines which trade-offs remain negotiable for adopting firms.

Modern failure management pipelines tightly couple task-specific models with modality-specific encoders, blocking reuse across systems. RuntimeSlicer proposes a unified runtime state representation that encodes metrics, traces, and logs into a single embedding via Unified Runtime Contrastive Learning, then adapts to downstream tasks through State-Aware Task-Oriented Tuning. The core value is decoupling representation learning from failure management tasks—if it generalizes, teams could freeze the embedding backbone and ship lightweight task heads.

PivotRL addresses the compute-generalization trade-off in agentic post-training by extracting "pivot" states—intermediate turns with high outcome variance—from existing SFT trajectories and applying functional-equivalence rewards rather than strict string matching. The method achieves comparable accuracy to end-to-end RL on SWE-Bench with roughly one-quarter the rollout cost, while avoiding the catastrophic forgetting typical of supervised fine-tuning on long-horizon tool-use tasks.

Understanding collective human intent from noisy, conflicting public discourse represents a frontier AI challenge that extends beyond individual instruction-following. This paper introduces COIN-Bench, a live-updating benchmark comprising 200k+ real consumer discussions across 1,400+ products, which operationalizes an Active Probing Paradigm requiring LLMs to act as meta-analysts and reconstruct chaotic feedback into structured questionnaires. The work matters because it shifts evaluation from transactional action prediction to hierarchical consensus synthesis, testing whether models can resolve contradictions and infer latent trends from swarm-like intelligence.

DMMRL tackles molecular property prediction by addressing two key challenges: entangled representations that obscure structure-property relationships and naïve multi-modal fusion that ignores inter-modal dependencies. The method uses variational autoencoders to decompose graph, sequence, and geometry features into shared (structure-relevant) and private (modality-specific) latent subspaces, enforcing orthogonality between them. A gated attention mechanism then fuses only the shared representations for downstream prediction.

Multi-modal tracking suffers from scarce paired training data, forcing reliance on RGB pre-trained models with lightweight fine-tuning. PATrack proposes a progressive adaptation framework using three complementary adapters—Modality-Dependent (MDA), Cross-Modality Entangled (CEA), and Head Adaptation (HA)—to bridge the domain gap between RGB and auxiliary modalities (Thermal, Depth, Event) at the intra-modal, inter-modal, and task levels. The approach decomposes features into frequency bands and uses fusion-guided cross-attention, yielding state-of-the-art results on LasHeR, RGBT234, and VisEvent benchmarks.

DomAgent addresses the challenge of generating code for specialized domains like truck control systems or data science libraries, where generic LLMs often fail due to lack of domain knowledge. The system combines structured knowledge graphs (top-down reasoning) with case-based retrieval (bottom-up learning) through a novel DomRetriever module that iteratively refines context via LLM-based review. Experiments on both the DS-1000 benchmark and a real-world truck software dataset demonstrate substantial improvements, enabling small 7B-8B parameter models to approach or exceed the performance of proprietary systems like GPT-4o.

This paper tackles the challenging problem of b-jet tagging at the LHC, particularly the difficult discrimination between bottom-quark jets (b-jets) and charm-quark jets (c-jets). The authors propose ECT (Edge Convolution Transformer), a hybrid deep learning architecture that combines local feature extraction via EdgeConv blocks with global context modeling through transformer self-attention. The work is motivated by the need for real-time flavor tagging in high-level trigger systems, where both accuracy and inference latency are critical.

Phase unwrapping recovers absolute interferometric phase from wrapped $2\pi$-modulo observations, but fails near surface-breaking faults that create abrupt discontinuities and in large-scale scenes that exceed GPU memory. This work proposes a diffusion-based framework that conditions on SNAPHU estimates and processes large interferograms via overlapping 256$\times$256 tiles with weighted averaging. It claims to handle fault-related phase jumps and scale to real-world Sentinel-1 interferograms without resizing.

GRPO training for LLM reasoning suffers from expensive rollouts and wasted compute on zero-variance prompts where all answers are correct or wrong. This paper proposes Prompt Replay, an overhead-free online method that buffers and reuses medium-difficulty prompts (pass rate near 0.5) to maximize gradient signal while staying on-policy by regenerating responses. By mixing replayed prompts with fresh samples and controlling reuse via cooldown steps and caps, the method aims to accelerate early training, though it eventually plateaus to baseline performance.

This paper tackles the lack of explicit memory mechanisms in transformers by introducing Mixture of Chapters (MoC)—a learned bank of 262K latent memory tokens accessed via cross-attention. To scale memory without prohibitive costs, the authors partition the bank into chapters and route each input sequence to a sparse subset (top-64), reducing complexity from $O(L \cdot N_m)$ to $O(L \cdot k \cdot T)$. The work demonstrates that explicit associative memory can serve as a new axis of scaling, showing improved knowledge retention when transitioning from pretraining to instruction fine-tuning.

LLMs with chain-of-thought reasoning can perform hidden internal computations across filler tokens, threatening AI safety by enabling obfuscated reasoning. This paper proposes an early-exit transformer architecture that trains models to truncate forward passes at intermediate layers when tokens are predictable, aiming to force reasoning into externalized CoT rather than internal activations. The approach uses self-distillation to calibrate exit probabilities followed by RL with a layer-depth penalty, showing on small models that adaptive depth reduction can maintain task performance while reducing computation.

This paper addresses reward hacking in reward-centric diffusion reinforcement learning (RDRL), where diffusion models exploit non-robust reward models to achieve high scores without actual perceptual quality improvements. The authors propose RSA-FT (Reward Sharpness-Aware Fine-Tuning), which mitigates hacking by flattening the reward landscape through joint perturbations in both image space (adversarial training) and parameter space (Sharpness-Aware Minimization). The method is plug-and-play, compatible with existing RDRL frameworks like ReFL and DRaFT, and shows consistent gains across SD1.5, SDXL, SD3, and Flux backbones.