Evidence-Seeking Inference

• Evidence-Seeking Inference is an approach that actively identifies and acquires only the most informative evidence needed to confidently answer queries under uncertainty.
• It underpins applications in long-context QA, clinical NLP, and autonomous agents by leveraging iterative, adaptive evidence collection strategies.
• Key innovations include selective evidence acquisition, confidence-based stopping rules, and cost–benefit optimization to enhance both accuracy and computational efficiency.

Evidence-Seeking Inference is an inference paradigm that prioritizes the targeted acquisition, selection, and utilization of supporting information (“evidence”) in order to efficiently and reliably answer queries, verify claims, or make decisions under uncertainty. Unlike static, passive inference—which processes all available data or performs holistic forward passes—evidence-seeking inference actively identifies, acquires, and evaluates minimal yet sufficient evidence, typically via iterative, agentic, or adaptive approaches. This paradigm underpins recent advances across long-context reasoning, open-domain QA, autonomous agents, clinical NLP, computational pathology, and Bayesian statistics.

1. Conceptual Foundations and Formal Problem Definition

Evidence-seeking inference is founded on the observation that relevant evidence is often sparse, distributed, or obscured within large, unstructured, or partially observed data. The core objective is not to consume all information indiscriminately, but to actively or adaptively seek the most informative, query-relevant evidence until a sufficiency criterion is met. Formally, the inference process is defined over:

• State space: Defined by the accumulated set of evidence $E$ and historical actions or plans $H$, reflecting all information gathered and prior decisions (Wang et al., 5 Dec 2025).
• Action space: Consists of evidence-seeking operations (e.g., observation plans, retrieval operations, tool calls), each parametrized by what evidence to seek, where (temporal/spatial/semantic region), and how (sampling, resolution, retrieval) (Wang et al., 5 Dec 2025, Zhu et al., 2021, Hua et al., 29 Dec 2025).
• Objective: Given a query $Q$, and an environment (e.g., a video $V$ or a corpus $D$), the goal is to acquire the minimum-cost evidence set $E$ needed to answer $Q$ with confidence $C(E, Q) \geq \tau_{\mathrm{conf}}$. Costs $c(\cdot)$ usually quantify compute, retrieval, or environmental interaction (Wang et al., 5 Dec 2025, Cai et al., 26 Dec 2025).
• Sufficiency: The inference halts when accumulated evidence justifies high-confidence decision-making, often via an explicit stopping rule (e.g., $C \geq \tau_{\mathrm{conf}}$) (Wang et al., 5 Dec 2025, Cai et al., 26 Dec 2025).
• Iterative design: The process is typically iterative, alternately proposing new evidence-seeking actions, acquiring observations, and reflecting on sufficiency (Wang et al., 5 Dec 2025, Malon, 2024).

2. Iterative and Agentic Evidence-Seeking Architectures

Modern evidence-seeking systems instantiate these principles in the form of agentic, modular, multi-stage architectures:

Active Video Perception (AVP)

AVP treats the video as an interactive environment. It employs an iterative Plan–Observe–Reflect pipeline:

• Planner: Generates a query-conditioned observation plan, maximizing expected information gain per cost.
• Observer: Samples targeted video segments based on the planner's specification, extracting structured, time-stamped evidence.
• Reflector: Assesses whether collected evidence suffices for answering the query, producing a confidence score and justification, and decides to halt or to trigger further planning (Wang et al., 5 Dec 2025).

PathFound for Pathology

PathFound operates via a three-stage loop:

• Initial Diagnosis: Drafts differential diagnoses and explicit evidence-seeking plans.
• Evidence Seeking: Selects new regions-of-interest via similarity metrics on learned morphological prototypes, requests additional tests, and integrates new findings.
• Final Decision: Aggregates all evidence for definitive diagnosis. The agent’s reasoner operates under reinforcement learning to optimize hypothesis-driven evidence acquisition (Hua et al., 29 Dec 2025).

Multi-hop and Adaptive QA

Open-domain QA implementations utilize iterative evidence-seeking via multi-hop retrieval—posing sub-questions to pursue missing evidence only when necessary, interleaving document retrieval and question reformulation until a claim can be verified or refuted with high adequacy (Malon, 2024, Zhu et al., 2021).

3. Evidence Selection, Sufficiency, and Efficiency

A hallmark of evidence-seeking inference is principled evidence-selection and rigorous sufficiency guarantees:

• Selective evidence acquisition: Rather than blanket retrieval or perception, evidence-seeking policies target locations most likely to contain relevant cues, often using expected entropy reduction or retrieval scores (Wang et al., 5 Dec 2025, Zhu et al., 2021).
• Confidence-based stopping: A quantified confidence metric $C(E,Q)$ determines halting—often computed by a learned module (e.g., an MLLM's softmax or explicit scoring prompt) (Wang et al., 5 Dec 2025, Cai et al., 26 Dec 2025).
• Cost–benefit tradeoff: Additional evidence is acquired only when the marginal gain in confidence surpasses its acquisition cost (e.g., $\frac{\Delta C}{c_{\mathrm{obs}}} \geq \kappa$) (Wang et al., 5 Dec 2025).
• Evidence minimality: Mechanisms such as greedy backward selection remove redundant evidence, curating a minimal decisive subset under a fixed completeness requirement (Cai et al., 26 Dec 2025).
• Robustness to noise and irrelevance: Specialized self-guided mechanisms, such as SelfElicit’s attention-guided highlighting, further filter noisy or distractor evidence at inference time (Liu et al., 12 Feb 2025).

Empirically, evidence-seeking inference yields substantial gains in both result quality (e.g., accuracy, justification adequacy) and system efficiency, enabling dramatic reductions in computation (observed tokens, wall-time, or environmental steps) relative to non-evidence-selective baselines (Wang et al., 5 Dec 2025, Malon, 2024, Cai et al., 26 Dec 2025).

4. Cross-Domain Implementations and Empirical Impact

Evidence-seeking inference has been instantiated and validated across diverse application domains:

Application Domain	Evidence-Seeking Mechanism	Empirical Gain
Long Video Understanding (AVP)	Plan–Observe–Reflect; query-driven targeted sampling	+5.7% accuracy, 18.4% compute vs. agents (Wang et al., 5 Dec 2025)
Computational Pathology (PathFound)	Hypothesis-driven RoI selection, RL for plan refinement	30–40% absolute accuracy gains (Hua et al., 29 Dec 2025)
Open-Domain QA	Multi-hop retrieval, iterative question reformulation	+0.045 label acc, +0.155 evidence adequacy (Malon, 2024)
Self-Verifying Agents (SmartSnap)	Proactive snapshot curation, 3C (Completeness, Conciseness, Creativity)	+26.1pp success rate (GUI task RL) (Cai et al., 26 Dec 2025)
LLM QA (SelfElicit)	Evidence highlighting by attention maps, prompt injection	+5–11.7% EM improvement (Liu et al., 12 Feb 2025)
Clinical NLP/NLI	Joint evidence snippet selection, confidence-driven aggregation	+0.26 macro-F₁ over baselines (DeYoung et al., 2020)

In pathology and clinical trial NLP, evidence-seeking methods substantially improve diagnosis or inference accuracy, especially under multi-evidence, ambiguity, or numerical reasoning regimes (Hua et al., 29 Dec 2025, Jullien et al., 2023, DeYoung et al., 2020). Similarly, in agentic environments (e.g., GUI automation), hybrid task/evidence policies and minimal snapshot curation enhance both performance and verifiability (Cai et al., 26 Dec 2025). In QA and LLM grounding, attention-driven self-guided evidence identification improves factuality without requiring additional training or expensive multi-pass extraction (Liu et al., 12 Feb 2025).

5. Statistical and Theoretical Foundations

Theoretical treatments unify evidence-seeking inference as a general methodology for principled reasoning, rooted in Bayesian, information-theoretic, and decision-theoretic frameworks:

• Bayesian evidence inference: The Bayesian evidence (marginal likelihood) $Z$—computed from posterior samples, as in hierarchical inference—quantifies the support for a model or hypothesis. Hierarchical DPGMM-based approximations infer $Z$ from samples, propagating approximant uncertainty (Rinaldi et al., 2024).
• Relative belief and evidence measures: Establishing inference built on a true measure of evidence, such as the relative belief ratio $RB(\psi|x) = \frac{\pi(\psi|x)}{\pi(\psi)}$, permits quantitative, direction-sensitive evaluation of how data support or undermine hypotheses, and underpins principled stopping and confidence assessment (Al-Labadi et al., 2018).
• Agentic and adaptive control: Evidence-seeking agents are often modeled as policies over sequential decision processes—MDPs or POMDPs—where querying, observing, and halting decisions are guided by expected information gain, state beliefs, or reward functions shaped to reward sufficient and efficient evidence gathering (Zhu et al., 2021, Hua et al., 29 Dec 2025).

6. Limitations, Open Challenges, and Future Directions

Despite strong empirical performance, evidence-seeking inference presents several open challenges:

• Multi-sentence and table-based evidence: Existing pipelines often rely on single best-sentence evidence, limiting performance on complex multi-hop or tabular inference (DeYoung et al., 2020, Jullien et al., 2023).
• Noisy, adversarial, and high-dimensional contexts: Scaling proactive evidence-seeking to extremely noisy or adversarial retrieval environments remains a key challenge (Malon, 2024).
• Joint inference and aggregation: Decoupling evidence selection from outcome inference can limit optimality; future methods may integrate end-to-end joint models for extraction and reasoning (DeYoung et al., 2020).
• Dynamics of open-ended environments: In dynamic, long-horizon environments, timely discovery of pivotal evidence and robust handling of delayed or missing information requires further methodology (Cai et al., 26 Dec 2025).
• Human–AI alignment: Evidence sufficiency, adequacy, and comprehensibility must eventually be aligned not just with computational proxies (confidence scores, reward surrogates), but with human-expert judgment and domain standards (Hua et al., 29 Dec 2025).

Potential future directions include incorporation of richer domain knowledge, adversarial data augmentation, plausibility-based verification for previously unseen claims, and expansion of modular agentic protocols to heterogenous, multi-modal environments.

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