TodayNewsResearchFieldsStacksMarketAnalytics
⌘K

Research

New papers ranked by attention, with their code, models and discussion linked. Pick a field on the left.

Field

Papers

Open problems

Rising research topics

Ask the research

Sample
ByteTrail · AI and developer-tools intelligence
NewsResearchStacksAPI
Fields · this week
All fields3749Multimodal593Agents561Frontier LLMs479Inference & serving288Image segmentation190Graph learning182Learning verifiable rewards1733D scene169Evals108Voice AI103Classifier & causal discovery88Forecasting time87Flow matching84Training & fine-tuning64Teacher policy distillation58All 32 fields
WeekMonthQuarterAll
NewestMost relevant

Papers

Rising research topics

Coming with more sources: more tagged papers
  • Federated learning50+38.9%
  • Agents561+9.1%
  • Frontier LLMs479−0.4%
  • Forecasting time87−3.3%
  • Character recognition OCR18−5.3%
  • Inference & serving288−6.5%
  • Image segmentation190−9.1%
  • 3D scene169−10.1%

Papers

  • arXiv cs.AIOct 20

    Semantic Observability for Autonomous Systems Diagnostics

    Jitendra Solanki, Anand Todkar, Ziran Min

    Modern distributed microservice deployments generate massive volumes of heterogeneous, multi-format observability logs across physically and logically partitioned infrastructure. Isolating root causes during cascading failures remains an arduous task for Site Reliability Engineers (SREs), bottlenecked by manual keyword filtering and disjointed temporal correlation across monitoring dashboards. We introduce Semantic Observability, an autonomous multi-agent framework that unifies disparate log streams into a dynamic, queryable Property Graph Database. By modeling physical infrastructure topologies, logical software dependencies, and chronological event sequences as attributed nodes and directed relations, it bridges the semantic gap inherent in flat, unstructured log repositories. Building on this unified substrate, we deploy a stateful multi-agent system orchestrated through Directed Cyclic Graphs (DCGs). The system autonomously navigates the topology, applying causal graph algorithms and temporal back-traversal to isolate the precise blast radius of anomalies. The pruned subgraph is then interpreted by a Large Language Model (LLM) reasoning engine to generate contextualized,…

    PDF
  • arXiv cs.SEOct 20

    InterWebCoder: Learning Interactive Webpage Generation from Executable Tests via Reinforcement Learning

    Jingyu Xiao, Dasen Dai, Yuxuan Wan, Jen-tse Huang, Yintong Huo +2

    Interactive webpages underpin modern applications, but reproducing both their visual appearance and multi-step behavior remains challenging for multimodal large language models (MLLMs). Scalable training data must capture user actions, state transitions, and expected outcomes, while real-world webpage code is often entangled with irrelevant scripts and external dependencies. Moreover, existing GUI-agent-based verification provides slow and potentially unstable functional feedback. To address these challenges, we propose InterWebCoder, an efficient framework for interactive webpage code generation. InterWebCoder introduces a Test-Specification-Guided and Execution-Verified Data Synthesis pipeline that records real-world interaction trajectories and synthesizes self-contained webpage code with executable test scripts. A failureattributed repair mechanism uses runtime evidence to locate and iteratively fix errors in either the webpage code or test script. We then combine test-ID-weighted supervised fine-tuning with group-relative policy optimization, directly optimizing functional correctness and visual fidelity using test pass rates and state-level visual similarity. This replaces…

    PDF
  • arXiv cs.AIOct 20

    Evolving Agent Interfaces

    Saaket Agashe, Yuzhe Yang, Gonzalo Gonzalez-Pumariega, Bhuvan Chandra Koduru, Chih-Lun Li +3

    Computer-use agents are limited by the interfaces humans give them. An agent may type a spreadsheet cell by cell when a single line of code would do, or print all two thousand lines of a file through a shell to find one function. Both the interfaces and the knowledge of when to use each are built by hand, which cannot keep pace with new applications, workflows, and models. We argue that interface design should itself be an agent capability. We introduce Agent S4, an autonomous computer-use scientist that builds Evolving Agent Interfaces from task experience. A meta-agent examines task agents' trajectories on synthetic training tasks, without success labels, and constructs and revises interfaces and usage knowledge in a repository. Population-based evolution then selects which repositories to develop further by their performance on validation tasks the meta-agent never sees, favoring changes that generalize beyond the seen tasks. On OSWorld V2, Agent S4, with Qwen3.8-27B and the evolved repository of interfaces, sets a new state of the art in open-source computer-use agents, reaching 21.3% binary score, 3.8 times the same model with fixed interfaces. This is above both GPT-5.5…

    PDF
  • arXiv cs.AIOct 20

    What Do RTL Repair Benchmarks Actually Measure?

    Yeyin Eva Zhu, Rishabh Ranawat

    RTL repair benchmarks should measure whether a model can fix broken RTL given a specification. To this end, we construct and hash-bind RTLRepairDataset, 5,784 (broken, fixed) training pairs; RTLRepairBench, 459 held-out cases with 374 syntactic and 85 behavioral mutations; and an evaluation harness supporting simulation and formal equivalence. We then use this infrastructure to identify two measurement confounds. First, simulation and formal scoring give opposite signs for the apparent effect of providing a specification in one evaluated cohort; reset-aware rescoring attributes almost all of the simulation-formal disagreement to reset and first-cycle comparison semantics. Second, on 274 mined model failures, the specification-only minus specification-conditioned recovery point estimate ranges from -8.5 to +18.3 percentage points across six models. Thus, a conventional repair score need not isolate whether the model used the faulty RTL. We translate these findings into evaluation controls: report initialization, reset, first-cycle, and unknown-value semantics; disclose oracle coverage and undecided cases; and include a specification-only baseline. Together, the resources and audit…

    PDF
  • arXiv cs.AIOct 20▲ 31

    Sage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition

    Wenjie Lou, Tianshuo Peng, Shiyang Feng, Shengji Tang, Runmin Ma +94

    In this report, we introduce Sage-A1.5, a 35B Mixture-of-Experts agentic model that builds on A1 to advance long-horizon problem solving through adaptive capability composition. Compared with A1, Sage-A1.5 learns not only to solve individual tasks, but also to organize work and revise its approach during execution. Sage-A1.5 highlights three improvements: agentic orchestration, verification, and open scientific discovery. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently and supporting efficient execution while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing the agent to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research…

    Code
  • arXiv cs.AIOct 20

    OptScientist: Automated Discovery of Optimization Laws

    Ma ZhenYao, Yue Liang, Jian-Qiao Zhu

    Many scientific phenomena are best understood as outcomes of underlying optimization problems, yet automated scientific discovery has largely focused on recovering equations that describe observed input-output patterns. We introduce OptScientist, a novel automated framework for discovering compact symbolic optimization laws directly from data. OptScientist comprises (i) a model that infers symbolic optimization problems by first learning to represent data as an optimization problem and then extracting its symbolic form, and (ii) an LLM agentic system embedding the model in a discovery process that integrates theoretical priors from scientific literature, discovers candidate laws, abstracts their underlying mechanisms, and suggests new data to collect. We first evaluate OptScientist on OptFeynman, a novel benchmark of 99 physics-derived optimization problems, where OptScientist recovers ground-truth laws for over 80% of the problems and remains robust to noisy contextual information, while substantially outperforming machine-learning, symbolic, and LLM-based model-discovery baselines. On human risky choice and central-bank monetary policy, OptScientist discovers compact symbolic…

    PDF
  • arXiv cs.AIOct 20

    Humans Correct Their Judgments Through Debate, but Weaker AI Models Do Not

    Maria Victoria Carro, Denise Alejandra Mester, Facundo Nieto, Oscar Agustin Stanchi, Guido Ernesto Bergman +12

    How can we effectively oversee AI systems that surpass human intelligence? AI Safety via Debate proposes that two AI systems argue opposing positions and a judge, either a human or a weaker model, decides which is correct. By facilitating supervisors in providing high-quality training signals, debate aims to encourage truthful and safe behavior from AI systems whose capabilities exceed our own. However, human judgment is not neutral: people often rely on biases which advanced AI systems may learn to exploit. Drawing on insights from collective intelligence and social cognition, this paper investigates whether debate can guide human and LLM supervisors toward the truth despite their prior beliefs. We evaluate several protocol configurations. We also explore setups inspired by the wisdom of the crowd such as the number and diversity of judges, as well as interaction mechanisms including deliberation and voting. Overall, we find that debate increases accuracy relative to prior beliefs for human judges, whereas the consultancy baseline, in which a single expert argues for one answer, does not. For weaker language models, however, the results are mixed: debate provides no comparable…

    PDF
  • arXiv cs.PLOct 20

    What is a Good Projection?: Correctness and Separation Results of End-Point Projections

    Jake Masters, Nobuko Yoshida

    \emph{Multiparty session types} (MPST) is a type discipline for ensuring distributed programs are implemented correctly \emph{by construction}. MPST starts by specifying a choreographic protocol as a \emph{global type} and \emph{projects} it onto each participant, generating \emph{local types} with a guarantee of type soundness and deadlock-freedom. A central issue of MPST is that its type soundness is fragile, as it must be proven each time with respect to a chosen projection algorithm under specific semantics with a specific program correctness goal. To overcome this problem, we propose general criteria for a \emph{good projection} -- a set of requirements for projection algorithms by which type soundness, deadlock-freedom and liveness of distributed programs are \emph{automatically} derived. Using the criteria, we prove that 8 distinct projections in the literature are good under both synchronous and asynchronous semantics, and that 4 are incorrect, failing type soundness. We extend the good projection criteria to mixed choice global types and asynchronous communication optimisations, proposing new ``good projection'' algorithms. We apply properties of good projections to…

    PDF
  • arXiv cs.CLOct 20

    OptionWorld: A Benchmark for Diagnosing Failures of LLMs as Long-Term Personal Assistants

    Ruoyu Zhang, Zhaocen Liu, Weiyi Tian, Melissa Feng, Shilong Liu

    Long-term assistants must follow unfinished tasks across days as information and circumstances change. Finding information or starting an action, however, does not ensure that the task gets completed. We introduce OptionWorld, a benchmark that traces information acquisition, judgments, actions, and outcomes in simulated daily-life tasks to identify where current LLMs fall short as long-term personal assistants. In runs with archived decision inputs, the key facts remain in the model's last input before the deadline in 35.5% of failed instances. Failure analysis identifies the three most frequent problems: needed information is not obtained or combined, available opportunities to act go unused, and agents impose restrictions that the task does not require. Three follow-up experiments show that supplying missing facts restores task success in some cases; action reminders can restart work while subsequent steps still miss the deadline; and correcting a supplied note that wrongly forbids contact restores permitted inquiry. These findings locate shortcomings in maintaining task state: connecting facts to unfinished work, following through before deadlines, and separating actual…

    PDF
  • arXiv cs.AIOct 20

    Proof-Carrying Contracts: Commitments AI Agents Cannot Break

    Carlo Ciliberto, Alessandro Rudi, Enrico Tassi

    AI agents increasingly act on behalf of people or other agents, purchasing services, exchanging information and delegating work. What an agent must respect is written in instructions, policies or agreements, but text alone cannot make it comply. It may make mistakes, be redirected by what it reads, or pursue different interests. For an economy of such agents to function, parties need agreements they can rely on without inspecting or trusting one another's agents. We introduce Proof-Carrying Contracts (PC2), a framework for making agreed constraints enforceable throughout an agent's interactions with a system. Before a requested operation can run, the agent must supply a machine-checkable proof that it preserves the agreed constraints in every case allowed by its model. An execution system called the Airlock checks the proof and controls access to the operation. The parties specify the conditions to preserve, while the agent supplies the reasoning needed to justify its decisions and remains free to adapt its strategy. This avoids having to design a separate permission policy and prove that every decision it permits is safe. We prove that the constraints hold at every instant…

    PDF
  • arXiv cs.AIOct 20

    Claims of emergence in AI should be grounded in information decomposition

    Christoph Riedl, Fernando E. Rosas

    When a language model develops capabilities not traceable to its individual components, when a multi-agent AI system outperforms each agent on its own, or when a human-AI pair succeeds where either fails in isolation, AI researchers describe it as a case of ``synergy'' or ``emergence''. However, these are usually informal assessments not supported by a theoretical framework, making these claims impossible to compare or even meaningfully dispute. Here we argue that \emph{information decomposition can serve as a unifying formal framework to operationalize assessments about synergy in AI systems}. Information decomposition provides a way to distinguish qualitatively different \emph{types} of information, including unique information available from individual components, redundant information shared across multiple components, and synergistic information accessible only from parts taken together. This gives claims of emergence a precise, falsifiable criterion: a system's behavior is emergent only if it carries synergistic information about task-relevant outcomes. We apply this lens to attention heads, autonomous agents, and human-AI teams, showing how it organizes existing knowledge…

    PDF
  • arXiv cs.AIOct 20

    Toolflation: Seller Agents Distort Tool Markets Through Spontaneous Generative Engine Optimization

    Zhenwei Tang, Di Bai, Jintao Liu, Xidong Wu, Qianfeng Wen +1

    Agents are starting to sell to other agents. Seller agents write the listings of tools, services and products, and buyer agents read these listings when they choose what to use. We show that seller agents told only to be selected more often practice generative engine optimization on their own, and that this distorts the market for sellers and for buyers. We build tool markets from real data tools on Amazon and Instagram and run every tool on every question, so we know which tools can answer each question. Under all five production models we test, sellers rewrite their descriptions to claim more and more fields, including fields that their tools return for fewer than half of the targets. We call this process toolflation. Nothing in the market limits it, and how far it goes depends on the model, as the number of claims that contradict the sellers' own evidence differs by an order of magnitude between models. Toolflation shifts the buyers' choices between sellers, and the most capable tool loses share in seven of the ten combinations of seller model and platform. Under four models, the choices stay with capable tools and buyer agents keep their task success. Under the fifth, sellers…

    PDF
  • arXiv cs.CROct 20

    A Digital Twin to Improve Cyber-Social Impact

    Vita Santa Barletta, Danilo Caivano, Gabriel Cellammare, Andrea Dimauro, Pasquale Mesagna +1

    The increasing complexity of Industrial Control Systems (ICS) and their integration with IT infrastructures has significantly expanded the cyber threat landscape. Traditional cybersecurity models, primarily based on the Confidentiality, Integrity, and Availability (CIA triad), fail to capture the broader societal and operational impacts of cyberattacks. This paper proposes a novel framework that integrates Digital Twin technology with a Cyber-Social Security model to simulate, analyze, and quantify cyber threats in critical infrastructures. Virtualized ICS environments built with Mininet and MiniCPS are used to model attack scenarios and assess their consequences across technical and social dimensions. At the core of the framework lies the Cyber-Social Vulnerability Scoring System (CSVSS), a quantitative metric that extends CVSS with a Social Impact dimension grounded in the Entity and Place pillars, producing a single, reproducible severity score that reflects real-world consequences. Crucially, the framework does not automate risk assessment but keeps the human analyst at its center: a Large Language Model (LLM) performs a first-pass analysis, mapping vulnerabilities and…

    PDF
  • arXiv cs.CROct 20

    After the Attacker Leaves: Benchmarking Carryover Attacks on LLM Agents

    Zhiyue Xu, Xukai Wang, Zhiyuan Huang, Mingjie Zhan, Jian Zhao +1

    Modern LLM agents persist state across sessions via two channels: long-lived memory files silently prepended to the system prompt, and scheduled tasks waking the agent without user input. A one-time indirect prompt injection committed to either channel can re-fire across sessions long after the attacker leaves, threats we term carryover attacks. Existing prompt-injection benchmarks evaluate attacks only in the session where malicious content is first observed, leaving the gate governing when and how often a planted payload later re-fires unmeasured. We present CarryoverBench: 12 personal-assistant scenarios and 101 attack tasks across three trigger channels: Instant, same-turn IPI; Autonomous, heartbeat-scheduled; and Latent, memory-resurfaced. A rule-based verifier scores planting and triggering as independent stages. Across five frontier models, planted payloads re-fire without measurable decay over five rounds; conditional on successful planting, the trigger stage often provides little additional protection. Porting the Latent channel to a second runtime, Hermes, attenuates but does not eliminate the effect, suggesting that carryover defense requires runtime-layer controls in…

    PDF
  • arXiv cs.LGOct 20

    Rewards for Budget-Aware Multi-Turn Agents That Adapt to Arbitrary Test-Time Constraints

    Tom A. Lamb, Adam Golinski, Eleonora Gualdoni, Sinead Williamson, Michael Kirchhof

    Large language model agents are often given an inference budget and asked to maximise success within it. A multi-turn agent has to allocate this budget across many environment interactions. An external harness can enforce the budget, but it remains unclear whether the agent itself can learn when and how much to spend. We ask whether agents can learn this allocation across dynamically specified budgets and which rewards best teach this behaviour. We adapt existing length penalties to budget constraints spanning multiple resource types, introduce new reward formulations, and compare eleven objectives in a shared training pipeline. We train Qwen3.5 models at 4B and 9B on AppWorld with budgets over tokens, API calls, and environment steps, and on the BabyAI gridworld with turn budgets. We train a separate policy for each environment and resource type, with each policy trained across budget values. Training with rewards that penalise overspending substantially improves within-budget success and natural compliance over untrained models and over training on task success alone, with agents learning to adapt their resource use to stated budgets and task difficulty. These gains extend to…

    PDF
  • arXiv cs.CROct 20

    MAS-Guard: Unified Guardrails from Individual Agents to Multi-Agent LLM Systems

    Mohammad Kachuee, Phu Mon Htut, Sheng Li, Nikolaos Pappas, Miguel Ballesteros Martinez +3

    Agentic AI extends large language models (LLMs) from generating responses to planning and executing complex workflows, substantially expanding their utility while exposing applications to unsafe actions, information leakage, and adversarial manipulation. Multi-agent systems further expand these capabilities through specialization and collaboration, but distributed authority, heterogeneous roles, and opaque or potentially compromised participants make safeguarding even more challenging. Existing defenses address important individual threats but provide fragmented coverage across local execution, system-level interactions, and enforcement mechanisms. We introduce MAS-Guard, a unified framework that closes this gap for safeguarding single- and multi-agent AI applications containing both instrumentable and opaque agents. Within instrumentable agents, the proposed local Agent Guardrail (A-Guard) layer applies task-specific experts to assess agentic events. At the system level, developer-owned Agent Charters and a Control Plane combine deterministic structural checks of routes, agent capabilities, and message formats with semantic assessments of roles and message content. These…

    PDF
  • arXiv cs.ROOct 20

    CAPSafe: A Benchmark for Contextual Action and Plan Safety Reasoning in Embodied Agents

    Youmna Farag, Svetlana Stoyanchev, Simon Keizer, Mohan Li, Rama Doddipatla

    Reasoning about safety is essential for embodied agents powered by large language models (LLMs). We introduce the Contextual Action and Plan Safety benchmark (CAPSafe), designed to evaluate safety reasoning at two levels: (1) an action-level dataset that tests whether a single action is safe given its environmental context, and (2) a plan-level dataset that evaluates whether agents can generate safe long-horizon plans for complex hazardous household tasks. CAPSafe models safety conditions where hazards are situational (inferred from object properties), multiple hazards can co-occur within the same environment, and each is annotated with a relative severity level for fine-grained analysis. Evaluating nine closed and open-source LLMs reveals a substantial gap between hazard recognition and safe long-horizon planning. Several models achieve high accuracy in recognizing unsafe actions (exceeding 90% in the best cases), yet safe long-horizon planning remains substantially harder. Gemini-3-pro achieves the highest safe success rate (78.19%), while other models remain below 65%. These results highlight that robust safety reasoning over long action sequences remains an open challenge.

    PDF
  • arXiv cs.CLOct 20

    Object-Oriented Harness: Evolving Agent Harnesses through Object-Oriented Design

    Gaotang Li, Bhavana Dalvi Mishra, Yifei Ming, Yanfei Chen, Zhongying CuiZhu +7

    Agent harnesses are executable code that specifies the prompts, tools, and control flow around a language model. A single global harness can struggle with heterogeneous tasks that require different execution strategies. In this work, we introduce Object-Oriented Harness (OO-Harness), a harness optimization framework that evolves a hierarchy of executable harness classes using object-oriented design. During training, an evolution agent uses execution records to create a family of specialized harnesses, each implemented as a subclass of the base or an existing specialized harness via inheritance. At test time, a harness router uses routing lessons accumulated during training to select a specific harness for each task. Across three long-horizon benchmarks spanning business workflows, workspace tasks, and tool use, OO-Harness achieves the best performance among the compared methods, with an absolute improvement of up to 15% over the base. Extensive ablation studies examine the contributions of its core components.

    PDF
  • arXiv cs.LOOct 20

    A General Construction of Strict Models in HoTT

    Niyousha Najmaei, Niels van der Weide

    Homotopy type theory (HoTT) provides a natural setting for studying the metatheory of type theory, as it comes with additional extensionality principles compared to ordinary Martin-Löf type theory. In particular, it is especially suitable for developing semantics of type theory, where one distinguishes two kinds of models: strict models, which give us sound interpretations, and pseudomodels. Pseudomodels are useful as they occur prevalently and can be turned into strict models by constructions called strictifications. However, the commonly used strictifications do not translate well to HoTT, as they require assumptions that are generally not satisfied by concrete examples of models. We show how to carry out the universe model construction, due to Voevodsky, in HoTT. Under this construction, every pseudomodel together with a universe type closed under enough type formers gives rise to a strict model. We also show how to interpret type formers in the resulting model assuming that the universe is closed under them. Finally, we demonstrate that this construction generalises existing instances of strict set models in the literature, and we apply it to more examples, yielding presheaf…

    PDF
  • arXiv cs.CROct 20

    Context-Aware Prompt Injection Detection via Small Language Models in RAG Pipelines: nokast-secureRAG

    Abhishek Rai

    Local Retrieval-Augmented Generation (RAG) systems are vulnerable to indirect prompt injection and retrieval poisoning, where malicious instructions in retrieved content are mistaken for trusted input. We present nokast-secureRAG, a roughly 0.5B-parameter small language model that acts as a semantic firewall between retrieval and generation. It jointly evaluates the user query and retrieved context, classifying the pair as safe, suspicious, or malicious. The model is trained using a teacher-judge-student pipeline: a 35B teacher generates 5,233 examples across four attack families, and an independent 120B judge re-labels them, achieving 95.0% agreement before student fine-tuning. On a held-out in-distribution test set, the model achieves 0.994 detection recall at a 0.026 false-positive rate and a 0.006 attack-success-rate proxy, outperforming a regex baseline, a public prompt-injection classifier, Llama Guard 3 1B, and its untuned version. Prompt-Guard-86M reaches 0.900 recall but has a 0.690 false-positive rate. On a judgment-flip test, where the same sentence is benign or malicious depending on context, the model correctly classifies both cases 75% of the time, outperforming the…

    PDF
Load more
PDF
Research · AI field

Agents

LLM agents: planning, tool use, multi-agent systems, computer/web agents and agent benchmarks.

Field page: people, tools, topics →
561papers this week+9.1% vs last week2396 this month

Open problems in Agents

  • Long-horizon task execution and evidence accumulation3 papers raise it · 2 address it

    Agents struggle to sustain multi-step reasoning, adapt strategies, and turn intermediate results into final deliverables in real-world tasks.

    • Scaling Agentic Data for Long-Horizon Terminal Intelligenceraises · addresses
    • FDE-Gym: The Last Mile of AI Deliveryraises · addresses
    • AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises
  • Realistic evaluation of agentic learning from experience1 paper raises it · 1 addresses it

    Existing benchmarks often test reasoning with pre-existing knowledge rather than learning from novel, unfamiliar environments.

    • Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?raises · addresses
  • Faithful agent development from natural-language requirements1 paper raises it · 1 addresses it

    Coding agents often fail to deliver agents that truly meet the specified requirements, not just appear to.

    • AgentSWE: Can Coding Agents Build the Agent You Actually Want?raises · addresses
  • Detecting when past context matters in long conversations1 paper raises it · 0 address it

    AI systems cannot reliably identify when historical context is relevant for current interactions in long-term companionship.

    • RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversationsraises
  • Scalable and realistic GUI agent training and evaluation1 paper raises it · 2 address it

    High cost and scarcity of realistic interactive environments limit progress in GUI agent training and evaluation.

    • MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environmentsraises · addresses
    • OpenGCUA: Toward a Fully Open Stack for Generalist Computer Useaddresses

Named by an AI from these papers' abstracts.