Gradual domain adaptation (GDA) tackles large distribution shifts by splitting them into ordered intermediate steps, but existing methods often suffer from error accumulation and unstable adversarial training. This paper proposes FlowGDA, a flow-matching framework that models gradual adaptation as continuous transport on a statistical manifold in a representation space, using time-dependent velocity fields learned via a simulation-free regression objective. By coupling local left-to-right transport with a source-anchored semantic consistency loss, the method aims to avoid the error accumulation and adversarial instability of discrete stepwise GDA methods.

Representation learning has improved sample efficiency in unconstrained MDPs, but extending it to constrained settings with unknown dynamics remains largely open. This paper studies low-rank constrained MDPs with continuous states where latent transition features must be learned online while controlling cumulative constraint violations. The authors propose two primal-dual algorithms that combine maximum-likelihood feature learning, uncertainty bonuses, and decaying $\epsilon$-greedy auxiliary probing to obtain the first sublinear regret and violation guarantees for this setting, with an explicit tradeoff between return-policy optimality and the safety cost of exploratory execution.

Autoregressive sequence models can suffer hidden future-token leakage when auxiliary operations—such as pooling, routing, or normalization—broadcast full-sequence summaries outside the causal attention mask. This paper audits these violations through prefix-invariance checks, a LeakScore metric, and layer-wise FirstLeakLayer localization, demonstrating that such leakage can produce false architectural gains: a CDR-AttnRes checkpoint drops teacher-forced validation loss from roughly 3.6 to 1.43 while its free-running generation perplexity explodes from under 20 to about 700. The core lesson is that teacher-forced validation loss must be paired with whole-model causal audits and open-ended generation diagnostics before claiming progress.

This paper adapts the Feature Selection Layer (FSL) into a post-hoc interpretability tool for pre-trained neural networks on tabular data. Instead of training an embedded feature weighting layer jointly with the network, the authors freeze the pre-trained model and insert a lightweight diagonal scaling layer that learns per-feature weights via fine-tuning. The approach aims to let practitioners retrofit already deployed models to obtain feature importance scores without retraining from scratch, bridging embedded feature selection and post-hoc attribution methods.

Feature flags become technical debt once fully rolled out, yet manually cleaning them at scale is prohibitively expensive. This paper describes a multi-agent LLM system deployed at DoorDash that automates stale flag removal by querying live rollout data via MCP, coordinating parallel cleanup agents in isolated git worktrees, and producing merge-ready pull requests. Evaluated on 50 stale flags across hundreds of repositories, the system produced usable PRs for 90% of cases at an average cost of $4.79 and 13.8 minutes per cleanup, offering one of the first quantitative industrial benchmarks for LLM-driven feature flag cleanup.

GUI agents struggle in long-horizon mobile tasks because they must repeatedly re-discover intermediate workflows from scratch. This paper proposes a subgoal-aware memory graph that segments past trajectories into contiguous action sequences sharing a single intent, then merges visually similar boundary screenshots into shared graph nodes and attaches annotated subgoal transitions as directed edges. The core claim is that this intermediate granularity is more reusable than either raw actions or full trajectories, which the authors test on a challenging mobile benchmark.

RiboBridge addresses the challenge of designing RNA molecules that bind to target protein structures when neither the native RNA sequence/structure nor the binding interface is known. Its core idea is to separate RNA conformation generation in a pretrained discrete structural space (RiboSphere) from protein-relative assembly via predicted cross-frame interaction geometry and rigid placement. This decomposition matters because high-quality protein–RNA complex data are scarce and existing methods entangle conformational sampling with interface discovery.

CausalCoT addresses the problem of unfaithful chain-of-thought reasoning in LLMs, where dense attention allows models to bypass their own generated rationales and exploit global context instead. It enforces procedural causal dependence by decomposing queries into a directed acyclic graph of sub-questions and evaluating each node in an isolated context containing only its direct parents. The framework promises more faithful explanations and precise test-time steerability via local interventions.

Tree-based speculative decoding wastes target-model compute when large retrieved candidate trees contain many low-quality branches. ValuSpec addresses this by introducing a plug-and-play verifier-side filter that scores branches with a lightweight model, calibrates a threshold $\tau_{\mathrm{tr}}$ from the target model's greedy trajectories without any parameter training, and prunes low-value candidates before expensive verification. This preserves exact greedy output identity and yields up to $2.38\times$ speedup, showing that candidate supply and verifier-side valuation are complementary optimization stages.

Peer review is buckling under submission volume, and LLM assistance is gaining traction. This paper asks how four current LLMs treat 200 already-published articles from top-tier journals such as Nature, Science, and Cell. The authors find that model recommendations are overwhelmingly harsh and starkly inconsistent: $605$ of $800$ reviews ($75.6\%$) recommended major revision or rejection, yet the rate for any single model ranged from $17.5\%$ to $100\%$ ($p < 0.001$). The takeaway is that LLM editorial signals are too variable to replace human judgment, though convergent criticisms may still be useful.

DIAL tackles the challenge of fine-grained control over instruction adherence—how strongly an LLM should follow instructions like "Keep it short." The core idea is to distill classifier-free guidance (CFG) into a lightweight, text-conditioned steering adapter that edits hidden states at a single layer. This matters because it promises scalable, efficient, adjustable adherence across diverse instructions without requiring labeled responses or per-instruction fitting.

Iterative combinatorial auctions require many costly rounds to discover clearing prices. This paper proposes FastCombo, an offline deep RL method that learns a recurrent graph policy to post sequential item prices with a single neural-network forward pass at deployment. The core insight is that maximizing immediate clearing probability can be arbitrarily suboptimal in expected rounds, so formulating the task as a POMDP and training over complete auction trajectories lets the policy acquire information that speeds up later clearing. Results show competitive clearing on standard CATS benchmarks with 300–900× lower online computation than a Bayesian baseline, though consistent round savings remain elusive outside a controlled one-item domain.

Instrumental convergence predicts that capable agents develop power-seeking subgoals, but it is unclear whether reinforcement learning with verifiable rewards (RLVR) monotonically increases such behavior in language models. This paper evaluates OLMo-3-7B-Think across sixteen post-training checkpoints using InstrumentalEval and finds a nonmonotonic trajectory: instrumental-like outputs rise during early RLVR, peak near step 200, and then fall below the SFT baseline by the final checkpoint. The result challenges simple "more RL equals more convergence" narratives and shows that checkpoint-level monitoring can reveal dynamics hidden by endpoint comparisons.

AgenticEvolve replaces the one-shot LLM variation operator in evolutionary program search with a persistent agent that edits code, runs self-designed experiments, and commits candidates for formal evaluation. By letting the agent test hypotheses locally between expensive evaluations, the framework aims to improve sample efficiency and final program quality on tasks ranging from online bin packing to symbolic regression. The authors report gains over a matched No-analysis ablation and over OpenEvolve, while noting that cost trade-offs depend heavily on model pricing.

This paper challenges how digital twins for type 1 diabetes are evaluated. Instead of the standard practice of scoring models by glucose trajectory error (RMSE), the authors propose measuring decision quality: whether a twin's simulated trajectories correctly rank candidate insulin treatments. They benchmark six methods on 30 virtual patients and show that a model can predict glucose accurately yet rank treatments no better than random, while a population-average baseline that ignores individual data ranks surprisingly well. The result is a convincing argument that clinical decision-support models need decision-aware evaluation.

Standard deep learning for spectroscopy treats spectra as fixed-grid intensity vectors, which makes models brittle when sampling grids, spectral ranges, or instruments change. This paper proposes to supply each spectral point as a pair of intensity and physical wavenumber, computing Transformer positional encodings directly from the wavenumber coordinate rather than the array index. Evaluated on in-house bacterial Raman spectra and a public 30-isolate benchmark, the approach improves robustness under partial spectral observation and on sampling grids never seen during training.

Retrieval scores for clinical RAG are often computed against a single annotated chunk per query, conflating retriever quality with the completeness of the ground-truth annotations. This paper introduces RAG-MAMA, a breast-lesion question-answering system, and proposes mapping canonical evidence spans to all containing chunks so that overlapping segments with equivalent facts are not scored as competing candidates. The authors report nearly perfect retrieval metrics ($\text{Recall}@3 = 0.94$) on an 18-query development set and cautiously frame the work as a small methodological baseline, though the narrow scale and lack of independent validation severely limit its conclusions.

Multi-agent LLM systems are becoming common, but it is hard to predict which model steers the group when agents disagree. This paper designs a minimalist naming game with meaningless labels to isolate consensus dynamics from pretrained knowledge or correctness. Across seven model families, the authors find that larger, more capable models dominate the final convention even when no answer is right, and they explain this with a simple energy landscape in which bigger models carry larger assertion weights.

The paper defines superintelligent software engineering as the discipline of building software with agents that exceed human reviewers, arguing that raw capability is necessary but insufficient without governance that scales mechanically. It proposes that correctness depends on decisions and contracts outside the code, that such knowledge decays, and that once agent throughput $r$ outstrips fixed human review capacity $c$, the share of stale-built changes caught by reading falls toward zero. An exploratory snapshot of 452 popular GitHub repositories finds widespread stale agent context files, and the paper introduces OSTEON, a released discipline of governed knowledge repositories, mechanical gates, and decay control. The author commendably marks OSTEON's unmeasured claims as opinion and frames a falsifiable research agenda.

This paper develops a decision-theoretic framework for valuing memory in language agents, treating memory not as raw information but as a resource whose worth depends on future decisions, access costs, and revision capabilities. The core idea is to track value from original history through retained state to realized output, using avoided task loss as a common unit and introducing risk-cost frontiers for recovery and revision under uncertain demand. This matters because current memory architectures optimize proxy metrics like relevance or compression ratio without guaranteeing that retained distinctions actually improve downstream decisions.