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Completion $\neq$ Collaboration: Scaling Collaborative Effort with Agents (2510.25744v2)
Published 29 Oct 2025 in cs.CL and cs.AI
Abstract: Current evaluations of agents remain centered around one-shot task completion, failing to account for the inherently iterative and collaborative nature of many real-world problems, where human goals are often underspecified and evolve. We argue for a shift from building and assessing task completion agents to developing collaborative agents, assessed not only by the quality of their final outputs but by how well they engage with and enhance human effort throughout the problem-solving process. To support this shift, we introduce collaborative effort scaling, a framework that captures how an agent's utility grows with increasing user involvement. Through case studies and simulated evaluations, we show that state-of-the-art agents often underperform in multi-turn, real-world scenarios, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. Collaborative effort scaling offers a lens for diagnosing agent behavior and guiding development toward more effective interactions.
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Explaining “Completion / Collaboration: Scaling Collaborative Effort with Agents”
Overview
This paper looks at how AI helpers (called “agents”) should work with people on real tasks. Instead of judging an AI only by whether it finishes a task in one try, the authors argue we should judge it by how well it collaborates with a person over multiple steps. They introduce a new way to measure this called “collaborative effort scaling,” which checks whether the AI gives more value as the person puts in more effort.
Key Questions and Goals
Here are the main things the paper asks and tries to do:
- How can we evaluate AI agents not just by their final answers, but by the quality of their teamwork with humans?
- When a person and an AI go back and forth, does the AI help more as the person puts in more effort?
- What makes a good “collaborator” AI in real-life situations like travel planning, data analysis, education, finance, and math research?
- How can we design and test agents to keep people engaged and make the overall results better?
Methods and Approach
To make these ideas concrete, the authors do three main things:
1) They define what “collaboration” looks like in everyday terms
- Think of the person and the AI taking turns, like players in a game. Each turn is a “round” where one side acts, then the other responds.
- The process has two parts:
- Initial request: The AI produces a first draft (like the first version of a travel plan).
- Refinement: The person and AI improve that draft together, step by step.
2) They introduce “collaborative effort scaling”
- Imagine a graph with two axes:
- User effort: how much thinking, reading, and responding the person does (for example, answering questions, checking details, or asking for changes).
- Utility (usefulness) of joint actions: how much the human–AI team actually achieves together (not just a fancy final answer, but explanations, transparency, and better decisions).
- Two qualities of a good collaborator AI:
- Interaction sustainability: If the person puts in more effort, the AI should produce more value, either immediately or in the final outcome.
- Maximum usability: The AI should keep the person engaged and avoid frustrating drop-offs, especially in complex or high-stakes tasks.
3) They run a simulated experiment in travel planning
- Task: Plan a multi-day trip with an AI, adjusting details like itinerary, transportation, and hotels based on evolving preferences.
- Simulated users: A LLM plays the role of the traveler, giving feedback and ratings.
- Agents tested: Different AI models (like Claude 3.5 Sonnet, Claude 4.0 Sonnet, GPT-4o, and Llama 3.1 70B) and two collaboration styles:
- One-stage planning: The agent plans and collaborates in a single loop.
- Two-stage planning: The agent first decides if it should collaborate now and then plans, adding an extra layer of structure.
- How they measured things:
- Performance (“utility”): Whether the travel plan makes sense and meets the user’s constraints.
- Effort: Number of rounds and amount of text each side generates (think of “tokens” as chunks of text similar to words).
What They Found and Why It Matters
Across real-world examples and simulations, several clear patterns emerged:
- Task-completion agents often disappoint in multi-turn work
- They produce polished answers too early, don’t show their reasoning, and don’t adapt when users change their goals.
- This happens in data analysis (hard-to-digest reports), travel planning (opaque choices), financial advising (failing to adjust to new preferences), education (giving answers instead of teaching), and math discovery (suggesting flawed proofs that waste time).
- More effort doesn’t always equal better results
- In the travel-planning simulation, performance improved for a few rounds, then plateaued.
- For some models (like GPT-4o and Llama 3.1 70B), collaborating didn’t beat doing it fully automatically—sometimes the agents got stuck in action loops.
- Structure helps less-capable models
- Two-stage planning boosted Claude 3.5 Sonnet, producing a better initial draft and better final results.
- For stronger models (like Claude 4.0 Sonnet), one-stage planning reached high performance faster and avoided larger “usability drops” (moments where the user might quit due to poor progress).
- Balance matters
- There’s a “sweet spot” in how much effort comes from the user vs. the agent. If the user has to do too much or the agent dominates too much, results get worse.
- Stronger models can perform well across a wider range of effort balances; weaker models need more scaffolding and clearer collaboration steps.
Why this is important: In real life, people often don’t know exactly what they want at the start. Good agents should help people think, explore, and refine their goals. Measuring only the final answer misses whether the AI actually helped the person understand and make better decisions along the way.
Implications and Impact
- Design agents as teammates, not just answer machines
- Agents should explain their choices, ask good questions, and help users refine goals over time.
- They should adapt to what the user understands, not just speed-run to a final product.
- Evaluate interactions, not just outcomes
- Track how utility grows as users put in effort.
- Watch for “usability drops” where people get frustrated and quit—those are red flags.
- Match collaboration style to model ability
- Stronger models may do fine with simpler interaction styles.
- Weaker models benefit from more structure (like two-stage planning, explicit checks, and step-wise reasoning).
- Practical benefits
- Better planning tools: more transparent itineraries and tailored recommendations.
- Improved learning tools: tutoring that adapts to the student’s level and builds understanding.
- Safer advice systems: financial guidance that revisits assumptions and aligns with evolving preferences.
- Smarter research helpers: agents that support, rather than slow down, scientific thinking.
Limitations to keep in mind: The main experiment focused on travel planning and used simulated users, not real people. Also, some tasks are fine to automate; the point is to identify when human involvement adds value and to help the AI make that involvement pay off.
Overall, the paper’s message is simple: Real-world problem solving is a team sport. If we want AI agents to be good teammates, we need to measure—and improve—how well they collaborate, not just how fast they finish.
Knowledge Gaps
Knowledge gaps, limitations, and open questions
Below is a single, focused list of what the paper leaves missing, uncertain, or unexplored, framed to be actionable for future research.
- Formalize human effort beyond quantity proxies: the paper uses counts of rounds and generated tokens; it lacks validated measures of cognitive load, sensemaking, comprehension, and confusion (e.g., time-on-task, NASA‑TLX, eye-tracking, correctness of mental models).
- Formalize agent utility beyond LM-judged output quality: utility conflates “final plan quality” with collaborative value (explanations, scaffolding, clarification handling) and does not capture intermediate knowledge gains, trust building, or transparency.
- Validate LM-as-judge metrics: commonsense/constraint pass rates and Likert “progress” scores are LM-generated; their reliability, bias, and alignment with human preferences are untested.
- Operationalize the POMDP framing: the paper defines a POMDP abstraction but does not specify state, belief updates, observation models, or control policies for mixed-initiative collaboration.
- Theoretical shape of collaborative effort scaling: no formal characterization of expected scaling curves (functional forms, monotonicity conditions, asymptotes) or conditions under which “interaction sustainability” should hold.
- Metric definitions need clarity and robustness: equations for overall utility, refinement gain, and usability drop are underspecified (e.g., tolerance threshold T selection), and ignore value from non-output actions (search, clarification) by copying previous scores.
- Sensitivity analyses are missing: no paper on how results change with different round caps (30), tolerance thresholds T, prompt choices, tool availability, or judge models.
- Generalization beyond travel planning: case studies span five domains, but the empirical evaluation only tests travel planning; the framework’s applicability to data analysis, education, math research, and financial advising remains unverified.
- Real-human validation: all experiments use simulated users; there is no measurement with real participants of engagement, drop-off reasons, trust dynamics, or long-term outcomes.
- Mapping tokens to real effort: token counts do not reflect human time, cognitive burden, or opportunity costs; the paper lacks a principled mapping from textual activity to human effort.
- Mixed-initiative policies are not instantiated: the paper argues agents should decide when to act, defer, or prompt, but offers no concrete algorithms, triggers, or policy-learning methods.
- Collaboration scaffolds lack design specifics: “one-stage” vs “two-stage” planning is compared, but scaffold components (e.g., constraint verification, guided decomposition, uncertainty flagging) are not systematically defined or ablated.
- Looping behavior analysis is incomplete: agents “get into loops” with certain models; there is no diagnostic taxonomy, detection methods, or mitigation strategies (e.g., loop breakers, progress checks).
- Global planning vs reactive behavior: the paper notes agents fail to develop coherent long-horizon plans; no concrete mechanisms (planning formalisms, memory structures, curricula) are proposed or evaluated to address this.
- Effort–utility “sweet spot” is observational: the discovered agent-to-user effort ratio sweet spots are not modeled or used to adapt agent behavior; no controller to target optimal ratios is proposed.
- Usability drop metric lacks user-grounded thresholds: the choice of no-progress tolerance T is arbitrary; no method to set T based on user preferences, task stakes, or empirical disengagement data.
- Trust, reliability, and safety are unmeasured: the framework does not quantify trust calibration, error recovery, content provenance, or domain safety (especially critical in finance and education).
- Long-term outcomes are not assessed: no measures of downstream task success (e.g., travel satisfaction), learning retention, or financial impacts; utility remains short-term and output-centric.
- Private information and partial observability remain theoretical: the paper motivates settings where users have private knowledge but does not implement or evaluate mechanisms that leverage it (e.g., belief modeling, elicitation strategies).
- Model capability profiling is ad hoc: claims that weaker models need more structure are based on few models; no standardized protocol to profile collaborative capabilities or to match scaffolds to model traits.
- External validity of judge prompts and evaluation scripts: the Xie et al. evaluation and internal prompts are not audited for domain coverage, leakage, or brittleness; reproducibility and robustness across alternative evaluators are unknown.
- Termination policies are underspecified: interactions end when “either party finds the task is done” or after 30 rounds; there is no principled termination criterion tied to utility gains, confidence, or user satisfaction.
- Memory and state management are not studied: agents’ use of memory, summarization, and context curation for sustained collaboration is not measured or designed.
- Transparency and assumption exposure lack metrics: the paper advocates exposing assumptions and reasoning but does not define metrics (e.g., assumption coverage, contradiction rates, explanation faithfulness) or evaluate them.
- Fairness and accessibility are ignored: differences across user expertise, culture, language, or disability in effort and utility are not considered; no accessibility or fairness metrics are included.
- Task-selection guidance is missing: while noting some tasks suit full automation, the paper offers no criteria to decide when collaboration is beneficial or how to switch between modes.
- Data and artifact availability: beyond pointing to repositories, there is no standardized benchmark of collaborative traces, annotations of effort/utility, or released protocols for human studies.
- Training to optimize collaborative scaling is unexplored: no methods (reinforcement learning, inverse reinforcement learning, preference learning) are proposed to learn policies that maximize interaction sustainability and usability.
- Error taxonomy and remediation pathways: across case studies, common failure modes (opaque outputs, misinterpreted intent, subtle logical flaws) are not cataloged with corresponding remediation strategies or evaluation hooks.
- Tool use and content provenance: the framework does not assess tool-selection quality, source reliability, or evidence tracking during collaboration; travel-planning errors from unreliable sources are unaddressed.
Practical Applications
Immediate Applications
The following applications can be deployed now by leveraging the paper’s collaborative effort scaling framework, case-paper insights, and the simulated evaluation tooling described.
- Instrument agent analytics with collaborative effort scaling metrics (software/AI platforms) — Application: Add “Overall Utility,” “Refinement Gain,” and “Usability Drop” to existing product analytics dashboards for LLM agents to audit multi-turn performance, detect early drop-off, and prioritize fixes that improve first-draft quality and sustain engagement. — Tools/Workflows: Log rounds, context windows, and updates; compute per-round performance (Pk); visualize scaling curves; implement stop-condition thresholds (T); use Collaborative-Gym-like traces. — Assumptions/Dependencies: Requires interaction logging, domain-specific utility definitions, token-level telemetry, and privacy-compliant data capture.
- Adopt two-stage planning scaffolds to improve first-draft quality (consumer apps, enterprise software) — Application: Introduce a “plan-first then act” gating step before the first substantial output, especially for models that benefit from structure (e.g., Claude 3.5), to increase initial utility and reduce downstream usability drop. — Tools/Workflows: Prompt templates for planning, explicit constraint verification checklists, “assumption ledger” before execution. — Assumptions/Dependencies: Adds latency and complexity; effectiveness is model-dependent and must be A/B tested against one-stage variants.
- Reasoning transparency and incremental sensemaking in data analysis tools (data science) — Application: Replace one-shot notebooks/reports with guided, stepwise analyses that expose assumptions, show intermediate plots, and invite user refinements rather than dumping complex code and conclusions. — Tools/Workflows: Colab/Jupyter extensions for “Explain steps,” “Assumptions surfaced,” and “What changed since last round?” with easy rollback and comparison. — Assumptions/Dependencies: Requires UI changes, richer provenance tracking, and a definition of interpretable intermediate utility (not just final metrics).
- Engagement sustainability monitors in agent products (customer support, fintech advisory, travel planning) — Application: Real-time detection of stalled progress (no improvement across T rounds) to trigger interventions: clarify, switch strategy (e.g., from autonomous to collaborative), or escalate to human. — Tools/Workflows: Drop-off risk heuristics; model-to-user effort ratio tracking (tokens); automated prompts to rebalance effort; human-in-the-loop escalation. — Assumptions/Dependencies: Depends on accurate progress measures, cost budgets to support additional rounds, and robust source reliability filtering.
- Mixed-initiative prompt policies and style tuning (software development, customer service) — Application: Profile models’ collaborative capabilities and tune their initiative (when to ask, defer, act) to hit the model-specific “sweet spot” in agent-to-user effort ratio that maximizes performance. — Tools/Workflows: Policy rules (e.g., ask clarifying questions before acting if utility stagnates), configurable initiative sliders, model profiling runs. — Assumptions/Dependencies: Requires per-model calibration; mis-tuned policies can increase effort without gains.
- Iterative intake and constraint re-evaluation in financial advising agents (finance) — Application: Replace single-shot risk assessments with dynamic re-evaluation as users learn; reconcile contradictory preferences; present rationale and trade-offs for allocations. — Tools/Workflows: Structured preference elicitation over rounds, “reflect and revise” prompts, contradiction detection, explanation cards for advice. — Assumptions/Dependencies: Compliance and liability considerations; demands reliable data and uncertainty handling.
- Adaptive AI tutoring that probes comprehension and balances short- and long-term goals (education) — Application: Tutors ask targeted questions, adjust explanations to student level, and deliberately trade off immediate homework help against concept mastery and transfer. — Tools/Workflows: Embedded “knowledge checks,” misconception detection, pacing controls, learning progress dashboards. — Assumptions/Dependencies: Needs measurement of learning signals; risks over-assistance if not carefully tuned; data privacy for student interactions.
- Transparent itinerary rationale and preference controls in travel agents (travel) — Application: Show why attractions were included/omitted; enable quick preference adjustment (e.g., pace, budget, themes); maintain source quality filters to preserve trust. — Tools/Workflows: Rationale panels, preference sliders, side-by-side itinerary comparisons, reliability indicators for sources. — Assumptions/Dependencies: Requires high-quality retrieval, robust content filtering, and clear handling of evolving user intent.
- Structured, uncertainty-aware collaboration in math discovery tools (academia/research) — Application: Agents flag uncertainty, validate intermediate proof steps, and maintain a proof attempt log that augments—rather than burdens—the researcher. — Tools/Workflows: Stepwise proof checkers, formal verification hooks, uncertainty tagging, error provenance. — Assumptions/Dependencies: Depends on access to formal systems or reliable proof checking; current models’ reasoning limits necessitate human verification.
- Procurement and internal governance updates to include collaboration metrics (policy, enterprise IT) — Application: Require vendors to report collaborative effort scaling scores, usability drop under defined tolerances, and first-draft utility in RFPs and risk assessments. — Tools/Workflows: Evaluation protocols, standardized reporting templates, acceptance thresholds for multi-turn utility. — Assumptions/Dependencies: Requires consensus on domain-specific metrics and acceptance criteria; may need third-party auditing.
Long-Term Applications
The following applications will benefit from further research, scaling, standardization, and/or regulatory alignment before broad deployment.
- Standardized benchmarks and certifications for collaborative agents (policy, academia, industry) — Application: Establish sector-specific and cross-domain benchmarks based on collaborative effort scaling (including cognitive load proxies), and certify agents for “interaction sustainability.” — Tools/Workflows: Public datasets, evaluation harnesses with real users, certification bodies and protocols. — Assumptions/Dependencies: Requires broad agreement on utility/effort proxies and access to multi-turn interaction data at scale.
- Adaptive controllers for mixed-initiative collaboration using POMDPs (software/AI platforms) — Application: Learn policies that decide when to prompt, plan, act, or defer, optimizing for utility gains relative to user effort; dynamically adjust scaffolds per model and task. — Tools/Workflows: Policy learning with multi-turn rewards, off-policy logs, simulation-to-deployment transfer. — Assumptions/Dependencies: Needs reliable reward shaping (beyond task completion), safe exploration, and strong runtime observability.
- Training objectives that optimize interaction sustainability (foundation models) — Application: Incorporate multi-turn, collaboration-aware rewards into RLHF/RLAIF (e.g., sustained utility gains, reduced usability drop), improving agents’ ability to leverage human effort. — Tools/Workflows: Datasets with high-quality interaction traces, multi-round evaluators, reward models for effort-utility balance. — Assumptions/Dependencies: Requires scalable data collection with consent; careful alignment to avoid gaming proxies.
- Sector-specific collaborative agents in high-stakes domains (healthcare, finance) — Application: Clinical decision support that scaffolds physician sensemaking (assumptions, alternative hypotheses, validation loops) and regulated fiduciary agents that reconcile evolving client goals with auditability and uncertainty quantification. — Tools/Workflows: Domain ontologies, verified toolchains (EHR, market data), provenance and audit logs, conformance with standards (e.g., HIPAA, MiFID). — Assumptions/Dependencies: Stringent accuracy, safety, and regulatory approvals; robust human oversight remains essential.
- Longitudinal AI tutoring integrated with curricula (education) — Application: Tutors that track concept mastery over semesters, dynamically rebalance assistance to maximize transfer learning and reduce learned helplessness. — Tools/Workflows: Curriculum-aligned knowledge graphs, teacher dashboards, policy-compliant data retention. — Assumptions/Dependencies: Requires school buy-in, data privacy frameworks, and evidence from randomized controlled trials.
- Multi-agent collaboration frameworks tuned for model capability disparities (software, robotics) — Application: Orchestrate agent teams with role-specific scaffolds (planner, verifier, explainer) that maintain the effort-utility “sweet spot,” improving robustness in complex tasks. — Tools/Workflows: Agent orchestration platforms, role-based policies, cross-agent evaluation with collaborative scaling metrics. — Assumptions/Dependencies: Inter-agent communication reliability and cost; careful failure-mode analysis to prevent loops.
- UI pattern libraries for collaboration diagnostics (product design) — Application: Standardize patterns for showing assumptions, intermediate milestones, effort balance indicators, and drop-off risk; enable consistent interventions across products. — Tools/Workflows: Design systems, component libraries, in-product “progress health” indicators. — Assumptions/Dependencies: Requires UX research to validate cognitive load proxies; needs product team adoption.
- Privacy-preserving, on-device effort telemetry and analytics (security/compliance) — Application: Track effort and utility locally, aggregate with differential privacy to inform collaboration tuning without exposing sensitive interaction data. — Tools/Workflows: On-device logging, secure aggregation, privacy budgets. — Assumptions/Dependencies: Hardware constraints, legal frameworks, and acceptable utility of noisy analytics.
- Third-party “CollabScore” marketplaces for agent selection (enterprise procurement) — Application: Independent ratings of agents’ collaborative profiles (e.g., first-draft utility, refinement gain, usability drop across tasks) to inform vendor decisions. — Tools/Workflows: Benchmark suites, standardized scoring, transparent methodologies. — Assumptions/Dependencies: Market incentives for transparency; avoidance of metric gaming; periodic re-testing as models evolve.
- Richer simulation environments with real user signals (research) — Application: Extend Collaborative-Gym with human-in-the-loop studies, cognitive load measures (timing, edits, confusion), and private-information tasks that emphasize irreducible human value. — Tools/Workflows: IRB-approved studies, instrumentation for behavioral traces, open datasets. — Assumptions/Dependencies: Funding and participant recruitment; ethical data practices; generalizability across domains.
Glossary
- Agent-to-user effort ratio: The relative balance of contributions between the agent and the user, often measured via token counts, used to analyze collaboration efficiency. Example: "agent-to-user effort ratios where performance peaks."
- Collaborative effort scaling: A framework for evaluating how an agent’s utility grows as user involvement increases across an interaction. Example: "we introduce collaborative effort scaling, a framework that captures how well an agent's utility impacts and scales with increasing user involvement."
- Collaborative-Gym: A simulation environment for studying asynchronous human–agent interactions in task settings. Example: "We use the Collaborative-Gym (Shao et al., 2024) environment that allows for asynchronous human and agent actions"
- Commonsense pass rate: An automatic metric indicating whether a generated plan aligns with general common-sense expectations. Example: "whether the derived plan satisfies common sense (commonsense pass rate) or user constraints (constraint pass rate)"
- Constraint pass rate: An automatic metric indicating whether a generated plan adheres to the user’s stated constraints. Example: "whether the derived plan satisfies common sense (commonsense pass rate) or user constraints (constraint pass rate)"
- Context window: The sequence of information available to the agent or human at a given action step. Example: "Each action is based on a corresponding context window c = [c(1) (2) .. , CT , C2 ,. (LT)]."
- Contextual tokens: The tokens a user needs to read or consider during interaction, used as a proxy for cognitive load. Example: "This can be enriched by summing the contextual tokens the human processes"
- Counterfactual measurement: An evaluation comparing actual outcomes to those that would have occurred under an alternative scenario. Example: "as a counterfactual measurement of the performance the agent can achieve if the user continues to interact with the agent."
- Dynamic control process: A perspective that treats collaboration management (when to act, defer, or prompt) as a control problem responsive to evolving signals. Example: "Achieving this requires modeling collaboration as a dynamic control process."
- Explicit constraint verification: A deliberate step or mechanism to check that proposed actions or outputs satisfy stated constraints. Example: "manual scaffolding—such as structured planning stages, explicit constraint verification, or guided decompo- sition—selectively"
- Fully autonomous baseline: A comparison condition where the agent operates without user collaboration. Example: "does not lead to better performance compared to the fully autonomous baseline."
- Guided decomposition: A structured technique for breaking down tasks into subproblems with guidance to support reliable progress. Example: "guided decompo- sition—selectively, based on where models demonstrate weaknesses in collaborative settings."
- Human-led rounds: Interaction segments initiated or driven by the human, used to quantify user effort. Example: "a basic measure of human effort could be the number of human-led rounds, |aH|."
- Initial request stage: The early phase of collaboration where the agent produces the first substantial draft of an output. Example: "The first is the initial request stage, during which the agent produces a preliminary draft of the output."
- Interaction scaffold: The structural supports (plans, prompts, checks) that shape and stabilize the collaboration process. Example: "the model's collaborative behavior is more heavily influenced by the interaction scaffold."
- Interaction sustainability: The property that additional user effort yields increased value over time. Example: "An ideal agent provides value as users spend more effort-'interaction sustainability'-and 'maximizes usability'"
- Joint action trace: The sequence of actions taken by both human and agent throughout the collaboration. Example: "We study the joint action trace between the human and agent"
- LLM agents: AI systems powered by LLMs that can reason, plan, and act in complex tasks. Example: "LLM agents capable of handling complex tasks"
- Latent preferences: User preferences that are not explicitly stated but can be elicited through interaction. Example: "the agent can elicit the user's latent preferences and constraints"
- Likert score: A rating on an ordered scale (e.g., 1–5) commonly used to measure attitudes or satisfaction. Example: "it produces a 5-point Likert score"
- Long-form reasoning: Extended, multi-step reasoning used by agents to tackle complex tasks. Example: "engage in long-form reasoning"
- Maximum usability: The ability of an agent to sustain productive engagement over longer interactions when needed. Example: "2. Maximum usability: Agents should encourage and sustain engagement across longer interaction trajecto- ries"
- Mixed-initiative interaction: A collaboration style where both human and agent can take the initiative to advance the task. Example: "Mixed-initiative interaction should follow effort-utility dynamics."
- One-stage planning agent: An agent that plans and collaborates within a single planning loop without a separate coordination phase. Example: "the one-stage planning agent reaches high performance more quickly"
- Overall utility: The maximum value an agent can provide across the full interaction, assuming sufficient user effort. Example: "1. Overall utility. Given unlimited human effort, what's the maximum value an agent can provide?"
- Partially Observable Markov Decision Process (POMDP): A formal framework for decision-making under uncertainty with limited observability. Example: "we describe the human-agent collaboration process with a Partially Observable Markov Decision Process (POMDP)"
- Performance score Pk: A per-round metric used as a proxy for the agent’s utility at interaction round k. Example: "we use the performance score Pk of round k as a stand-in for utility"
- ReAct framework: An agent design pattern that interleaves reasoning and acting (tool use) for problem solving. Example: "an automated agent implementation based on the ReAct framework (Yao et al., 2023)"
- Recursive problem-solving approach: A tendency to focus on immediate subproblems repeatedly without maintaining a coherent global plan. Example: "reliance on a seemingly recursive problem-solving approach"
- Refinement gain: The performance improvement achieved during the refinement stage after the first major output. Example: "2. Refinement gain. Furthermore, ... We define G as the performance improvement after the first major update"
- Refinement stage: The phase after the initial draft when the agent iteratively improves the output based on feedback. Example: "The process then transitions into a refinement stage, where the agents iteratively adjust and improve the output in response to human feedback."
- Sensemaking: The iterative process of interpreting options and rationales to build understanding for decision-making. Example: "Support iterative sensemaking of travel options."
- Structured planning stages: Deliberate phases in an agent’s workflow that organize planning and collaboration. Example: "manual scaffolding—such as structured planning stages, explicit constraint verification, or guided decompo- sition—selectively"
- Tolerance threshold T: A parameter specifying how many unproductive rounds are tolerated before stopping the interaction. Example: "according to certain no-progress tolerance, defined by a tolerance threshold T."
- Two-stage planning agent: An agent that includes an additional planning step to decide when and how to collaborate before acting. Example: "The difference between the one- and two-stage planning agent is that the latter incorporates an additional planning step"
- Usability drop: The loss in achievable performance due to user disengagement when progress stalls during collaboration. Example: "3. Usability drop. We formalize the observation that when an agent fails to make consistent progress in the collaboration, the user may stop interacting"
- Utility of Joint Actions: The value produced by the combined human–agent team during collaboration. Example: "Utility of Joint Actions - how much the joint human and agent team can accomplish together"
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