AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to new tasks. However, existing optimization methods typically require ground-truth validation sets, yet such labeled data is difficult to acquire in practical deployment settings. To address this problem, we introduce Retrospective Harness Optimization (RHO), a self-supervised method that optimizes the agent harness using only past trajectories. Specifically, RHO selects a diverse coreset of challenging tasks from past trajectories and re-solves them in parallel. The agent analyzes these rollouts using self-validation and self-consistency, then generates candidate harness updates and selects the most effective one by its own pairwise self-preference. We evaluate RHO across three diverse domains, spanning software engineering, technical work, and knowledge work. Notably, a single optimization round improves the pass rate on SWE-Bench Pro from 59% to 78% without any external grading. Furthermore, our analysis demonstrates that RHO effectively targets prior failure modes. As a result, the optimized harness alters the agent’s behavior patterns and sustains higher accuracy during long-horizon sessions. Code is available at https://github.com/wbopan/retro-harness and the project website at https://paper-rho.wenbo.io.

Google’s Agent-to-Agent (A2A) protocol transforms multi-agent collaboration from a closed pipeline into an open and dynamic ecosystem. A2A-based multi-agent systems typically depend on a central orchestrator to coordinate task allocation across agents. However, this paradigm exposes a new attack surface at the agent-orchestration layer: an agent provider can forge an adversarial agent profile to bias the orchestrator’s decisions. We find this risk and propose OrcaJack, an agent orchestration hijacking attack. By injecting a deceptively appealing agent profile into the agent pool, OrcaJack causes user queries in a target domain to be consistently routed to the malicious agent. We formulate the adversarial agent profile construction process as a discrete optimization problem and solve it with a domain-skill-driven genetic algorithm. Across multiple domain benchmarks and orchestrator LLMs, Orcajack can reach up to 100% routing rate, while simple pool-side defenses fail to flag the crafted profiles. Our work is the first to expose the over-trust of agent-declared capabilities in modern agent discovery, motivating pool-side validation defenses.

Data attribution methods often compress the effect of each training data point into a single score, computed either at a final checkpoint or by aggregating quantities along the training trajectory. This static view hides how the contribution of a training data point changes during training. To overcome this limitation, we propose the Window-level Influence Estimator (WIE), a framework for estimating the contribution of a training data point zⱼ over a user-specified training window [t₁, t₂]. WIE models a leave-one-out-style counterfactual under a first-order linearization of SGD. An influence propagator P[t] = I − ηₜH[t] at each step models how parameter deviation is amplified or dampened by the loss landscape. Empirically, the window-level estimator reveals four distinct influence patterns: Early Influencer, Late Bloomer, Stable, Highly Fluctuating. Across multiple benchmarks, WIE improves mislabeled-data detection over the considered static and trajectory-based baselines. On BERT-IMDB with 40% label noise, WIE outperforms these baselines across the evaluated inspection ratios. On ViT-EMNIST, WIE reaches 70% recall for mislabeled data while inspecting up to 7.5× fewer candidates than TracIn requires to reach the same recall.

Large language models (LLMs) demonstrate significant capabilities in many natural language processing. However, their performance in machine translation is still behind the models that are specially trained for machine translation with an encoder-decoder architecture. This paper investigates how to improve neural machine translation (NMT) with LLMs. Our proposal is based on an empirical insight that NMT gets worse fluency than human translation. We propose to use LLMs to enhance the fluency of NMT’s generation by integrating a language model at the target side. we use contrastive learning to constrain fluency so that it does not exceed the LLMs. Our experiments on three language pairs show that this method can improve the performance of NMT. Our empirical analysis further demonstrates that this method improves the fluency at the target side. Our experiments also show that some straightforward post-processing methods using LLMs, such as re-ranking and refinement, are not effective.

In recent years, Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence. However, training these models from scratch requires substantial computational resources and vast amounts of text data. In this paper, we explores an alternative approach to constructing a LLM for a new language by continually pre-training (CPT) from existing pre-trained LLMs, instead of using randomly initialized parameters. Based on parallel experiments on 40 model sizes ranging from 40M to 5B parameters, we find that 1) CPT converges faster and saves significant resources in a scalable manner. 2) CPT adheres to an extended scaling law derived from with a joint data-parameter scaling term. 3) The compute-optimal data-parameter allocation for CPT markedly differs based on our estimated scaling factors. 4) The effectiveness of transfer scale is influenced by training duration and linguistic properties, while robust to data replaying, a method that effectively mitigates catastrophic forgetting in CPT. We hope our findings provide deeper insights into the transferability of LLMs at scale for the research community.

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.