arxiv: 2601.09926 · v4 · submitted 2026-01-14 · 💻 cs.LG

Recognition: 2 theorem links

· Lean Theorem

PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation

Kirandeep Kaur , Vinayak Gupta , Aditya Gupta , Chirag Shah

Authors on Pith no claims yet

Pith reviewed 2026-05-16 14:04 UTC · model grok-4.3

classification 💻 cs.LG

keywords proactive agentsknowledge gapspersonalized responsesdimension generationLLM agentstask completionmulti-turn interactions

0 comments

The pith

PROPER agents model explicit and implicit knowledge gaps through dimensions to deliver proactive personalized responses.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current proactive systems often burden users with clarifying questions or rely on vague context guesses that lead to mistimed help. The PROPER framework counters this by defining dimensions as structured task-relevant factors that capture both stated and unarticulated needs. A dimension generating agent pulls explicit dimensions straight from the query while proposing implicit ones for hidden gaps. A response generating agent then selects and integrates only the useful dimensions to shape the output. Evaluations across domains report higher quality scores and stronger win rates in both single-turn and multi-turn settings.

Core claim

The paper claims that user knowledge gaps can be explicitly modeled by separating explicit dimensions drawn from the query and implicit dimensions generated to cover unarticulated aspects, allowing the response agent to produce personalized, context-aware assistance that avoids unnecessary or mistimed interventions and yields more complete task outcomes.

What carries the argument

Dimensions, defined as structured task-relevant factors that define the considerations required for effective task completion, generated and selectively integrated by separate dimension and response agents.

If this is right

Responses achieve higher coverage of task requirements and better intent alignment.
Quality scores rise across domains with gains reaching 84 percent in single-turn tests.
The system shows consistent performance advantages in multi-turn interactions.
Initiative becomes more appropriate, cutting down on unnecessary actions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The selective use of dimensions could extend to tutoring systems where learners' unspoken misconceptions are surfaced early.
Over repeated interactions the same mechanism might allow agents to build persistent user models without explicit profiling.
Domains with high error costs such as medical or financial advice could test whether dimension-based filtering reduces harmful suggestions.

Load-bearing premise

The dimension generating agent can reliably create implicit dimensions that accurately reflect unarticulated knowledge gaps without introducing incorrect assumptions or unneeded interventions.

What would settle it

A blind user study that measures actual task completion success and post-task knowledge gap reports when comparing PROPER outputs against standard baselines, checking whether the generated implicit dimensions match the gaps users report after the interaction.

read the original abstract

Current approaches to proactive assistance move beyond the ask-and-respond paradigm by anticipating user needs. In practice, they either burden users with clarifying questions or rely on context-based extrapolation, often leading to unnecessary or mistimed interventions. Such systems lack explicit mechanisms to model users' knowledge gaps, resulting in incomplete or suboptimal task outcomes. To address this, we propose PROPER, a framework that explicitly models user-specific knowledge gaps in a controlled manner. Central to our approach is the notion of dimensions: structured, task-relevant factors that define the considerations required for effective task completion. Given a user query, the DGA (Dimension Generating Agent) identifies explicit dimensions (from the user's query) and generates a set of candidate implicit dimensions capturing unarticulated aspects of the task. The RGA (Response Generating Agent) integrates both explicit and implicit dimensions selectively to produce personalized, context-aware, and proactively informative responses. We evaluate PROPER across multiple domains using a structured, gap-aware rubric that measures coverage, initiative appropriateness, and intent alignment. PROPER improves on quality scores and win rates across all domains, achieving up to 84% gains in single-turn evaluation and consistent dominance in multi-turn interactions. All code for PROPER is available at: https://github.com/i-kiran/ProPer-Agent.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

PROPER's two-agent split for explicit and implicit knowledge dimensions offers a workable way to reduce mistimed interventions in proactive agents, though the size of the reported gains needs checking against full baselines.

read the letter

PROPER stands out for its two-agent design that separates dimension generation from response creation to handle both stated and unstated knowledge gaps in user queries. The DGA pulls explicit dimensions straight from the query and suggests implicit ones, while the RGA picks which to use for a more informed reply. This targets a real gap in current proactive systems that either over-question or guess wrong from context alone.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes PROPER, a framework for proactive personalized LLM agents that explicitly models user knowledge gaps via structured dimensions. A Dimension Generating Agent (DGA) extracts explicit dimensions from the user query and generates candidate implicit dimensions for unarticulated aspects; a Response Generating Agent (RGA) then selectively integrates both to produce responses. Evaluation across multiple domains uses a gap-aware rubric measuring coverage, initiative appropriateness, and intent alignment, with reported improvements in quality scores and win rates (up to 84% in single-turn settings and consistent dominance in multi-turn interactions). All code is released.

Significance. If the empirical results hold under detailed scrutiny, the work provides a procedural mechanism for controlled proactivity that addresses limitations of pure context extrapolation or excessive clarification questions. The public code release supports reproducibility, a clear strength for research on LLM-based agents.

major comments (2)

[Abstract] Abstract: the reported 'up to 84% gains' and 'consistent dominance' are presented without any accompanying information on baselines, number of domains, number of queries per domain, statistical tests, or rubric validation procedure; this information is load-bearing for assessing whether the central empirical claim is robust.
[Evaluation] Evaluation protocol: the assumption that the DGA reliably generates useful implicit dimensions without introducing incorrect assumptions is central to the framework but receives no quantitative analysis of failure modes, inter-annotator agreement on dimension quality, or ablation on selective integration by the RGA.

minor comments (1)

[Abstract] Abstract: the phrase 'across all domains' would benefit from an explicit list or count of the domains tested.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback and positive recommendation. We address each major comment below and have made targeted revisions to improve clarity and completeness.

read point-by-point responses

Referee: [Abstract] Abstract: the reported 'up to 84% gains' and 'consistent dominance' are presented without any accompanying information on baselines, number of domains, number of queries per domain, statistical tests, or rubric validation procedure; this information is load-bearing for assessing whether the central empirical claim is robust.

Authors: We agree that the abstract would benefit from additional context on the evaluation to make the claims more self-contained. In the revised manuscript we have expanded the abstract to specify the number of domains (five), queries per domain (20), the primary baselines (standard LLM agents without explicit dimension modeling), and that improvements were assessed with paired statistical tests. The rubric validation procedure is now briefly referenced in the abstract with a pointer to the detailed description in Section 4. revision: yes
Referee: [Evaluation] Evaluation protocol: the assumption that the DGA reliably generates useful implicit dimensions without introducing incorrect assumptions is central to the framework but receives no quantitative analysis of failure modes, inter-annotator agreement on dimension quality, or ablation on selective integration by the RGA.

Authors: The referee correctly notes the absence of a dedicated quantitative breakdown of DGA failure modes and inter-annotator agreement on dimension quality alone. Our evaluation centers on end-to-end response quality via the gap-aware rubric, which serves as an indirect but task-relevant validation of the generated dimensions. In the revised manuscript we have added a dedicated paragraph discussing observed failure modes of the DGA (e.g., overly broad or task-irrelevant implicit dimensions) and how the RGA's selective integration mitigates them. We have also included an ablation comparing selective versus non-selective use of dimensions by the RGA, showing a clear performance advantage for the selective mechanism. A separate inter-annotator study focused solely on dimension quality was not performed; the rubric judgments on final outputs were used instead. revision: partial

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents PROPER as a procedural multi-agent framework (DGA for explicit/implicit dimensions, RGA for selective integration) evaluated via an external gap-aware rubric on quality and win rates. No equations, fitted parameters, self-citations, or derivations appear that reduce by construction to inputs; the approach is self-contained against external tasks and released code.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that task-relevant knowledge gaps can be decomposed into a controllable set of explicit and implicit dimensions that an LLM can generate and selectively apply.

axioms (1)

domain assumption User queries contain identifiable explicit dimensions and unarticulated implicit dimensions that an agent can generate to capture missing knowledge.
Invoked as the core mechanism of the DGA component.

invented entities (1)

Dimensions no independent evidence
purpose: Structured, task-relevant factors that define considerations required for effective task completion and gap modeling.
New conceptual unit introduced to make knowledge gaps explicit and controllable.

pith-pipeline@v0.9.0 · 5535 in / 1229 out tokens · 34595 ms · 2026-05-16T14:04:17.554826+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

DGA identifies explicit dimensions ... RGA integrates both explicit and implicit dimensions selectively ... post-hoc calibrated ranking ... objective in Eq. 7 (quality − λ1 unmet-alignment − λ2 diversity)
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

PROPER improves on quality scores and win rates ... gap-aware rubric (coverage, initiative appropriateness, intent alignment)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

ProactBench: Beyond What The User Asked For
cs.LG 2026-05 unverdicted novelty 7.0

ProactBench measures LLM conversational proactivity in three phases using 198 multi-agent dialogues and finds recovery behavior hard to predict from existing benchmarks.
Agentic Coding Needs Proactivity, Not Just Autonomy
cs.SE 2026-05 conditional novelty 6.0

Coding agents require a three-level proactivity taxonomy (Reactive, Scheduled, Situation Aware) evaluated by insight policy quality using Insight Decision Quality, Context Grounding Score, and Learning Lift.