X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Digital Human Attention

• URL: http://arxiv.org/abs/2605.15505v2
• Date: Wed, 20 May 2026 23:24:18 GMT
• Title: X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Digital Human Attention
• Abstract summary: We present X-SYNTH, a framework for enterprise context synthesis grounded in digital human attention.<n>X-SYNTH models each individual's behavioral baseline as a Digital Twin Signature (DTS)<n>A four-stage pipeline assembles ranked context grounded in behavioral patterns rather than query embeddings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In enterprise operations, the context required for an AI agent task is scattered across systems of record, static information stores, and communication channels. What is stored is system state, a lossy representation of the work that actually happened. The prevailing approach retrieves by matching request content to what is stored; for narrow requests this works well. But synthesis quality depends on knowing what to surface and how to interpret it: knowledge specific to each organization, team, and individual, present in behavioral patterns, absent from any retrieval index. For the agentic task of proposing enterprise-valuable leads to sellers, this approach breaks down: True Lead Rate is low, False Lead Rate is high, and the model has no mechanism to improve. We present X-SYNTH, a framework for enterprise context synthesis grounded in digital human attention, the digitally observable interaction signatures of each worker, encoding what they did, the sequence in which they did it, and implicit reward signals. Behavioral traces preceding positive outcomes are distinguishable from those that did not, without external labeling. X-SYNTH models each individual's behavioral baseline as a Digital Twin Signature (DTS) and selects among seven attention filters, Proportional, Inverse, Differential, Recurrent, Comparative, Sequential, and Collective, per individual and per query, to identify causally relevant activity signatures. A four-stage pipeline assembles ranked context grounded in behavioral patterns rather than query embeddings. A frontier model unaided achieves 9.5% True Lead Rate (TLR) with 90.5% False Lead Rate (FLR). Augmented with X-SYNTH, TLR rises to 61.9% (6.5x) while FLR falls to 18.8%. Enterprise context synthesis is not a retrieval problem. It is a relevance problem, and digital human attention is its most reliable ground truth.

Related papers

• Token Optimization and Context Window Management in Multi-Agent AI Workflows [0.0]
  Multi-agent AI synthesis are limited not only by model quality but by token cost, latency, and context-window quality.<n>This paper presents a practitioner framework for token optimization and context-window management.<n>In production they cut measured cold-load latency to 61-116 seconds from an operational baseline of roughly 3.5-10.5 minutes, with an estimated 60-70% token reduction.
  arXiv  Detail & Related papers  (2026-08-17T22:56:50Z)
• A Dynamic-Semantics Framework for Grounding Human Referring Expressions in Visual Perceptual Data [0.0]
  We present a framework that externalizes pact state into three explicit, inspectable sets of referent-object bindings.<n>We evaluate the framework on the Stanford Repeated Reference Game corpus.
  arXiv  Detail & Related papers  (2026-08-09T12:14:10Z)
• NTDH: Complex Reasoning for Comprehensive Affective Analysis [28.280253328311346]
  Comprehensive affective analysis is challenging for two reasons.<n>It spans heterogeneous prediction tasks with continuous, ordinal, and multi-label outputs.<n>We recast the task as a complex-reasoning problem.<n>This is the first such treatment covering both sentiment and emotion.
  arXiv  Detail & Related papers  (2026-08-05T18:59:43Z)
• Context Graphs for Proactive Enterprise Agents [2.3097706741644686]
  This paper argues that genuine enterprise productivity gains require proactive agents that surface relevant, actionable information to workers before they ask.<n>We propose the Context Graph, a live relational data structure that models enterprise entities, their relationships, and state transitions over time.<n>We provide a complete end-to-end Python implementation using NetworkX and the Anthropic Claude API.
  arXiv  Detail & Related papers  (2026-07-04T14:37:23Z)
• How to Interpret Agent Behavior [56.59836196946289]
  We introduce ACT*ONOMY, a taxonomy for describing and analyzing agent behavior at runtime.<n>By providing a shared vocabulary, ACT*ONOMY helps researchers, agent designers, and end users interpret agent behavior more consistently.
  arXiv  Detail & Related papers  (2026-05-13T14:52:40Z)
• Towards Multi-Agent Autonomous Reasoning in Hydrodynamics [0.06999740786886537]
  We present a multi-agent system (MAS) prototype for hydrodynamics in which specialized agents are coordinated through a Layer Execution Graph (LEG)<n>A planner agent constructs query-specific execution topologies from natural-language routings that capture domain knowledge without hard-coding it as rigid control logic.<n>A reporter agent synthesizes the final response, while the runtime logs provenance for every tool invocation to support auditability.
  arXiv  Detail & Related papers  (2026-05-01T21:17:55Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• AgentSynth: Scalable Task Generation for Generalist Computer-Use Agents [60.881609323604685]
  Agent Synth is a scalable and cost-efficient pipeline for automatically synthesizing high-quality tasks and trajectory datasets.<n>Our pipeline achieves a low average cost of $0.60 per trajectory, orders of magnitude cheaper than human annotations.
  arXiv  Detail & Related papers  (2025-06-17T05:46:52Z)
• SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation [55.2480439325792]
  We study the synthesis of six datasets, covering topic classification, sentiment analysis, tone detection, and humor. We find that SynthesizRR greatly improves lexical and semantic diversity, similarity to human-written text, and distillation performance.
  arXiv  Detail & Related papers  (2024-05-16T12:22:41Z)
• Robust Deep Learning from Crowds with Belief Propagation [6.643082745560235]
  A graphical model representing local dependencies between workers and tasks provides a principled way of reasoning over the true labels from the noisy answers. One needs a predictive model working on unseen data directly from crowdsourced datasets instead of the true labels in many cases. We propose a new data-generating process, where a neural network generates the true labels from task features.
  arXiv  Detail & Related papers  (2021-11-01T07:20:16Z)
• Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System [24.227068548489385]
  In this paper, we argue that consistency problem is more urgent in task-oriented domain. To facilitate the research, we introduce CI-ToD, a novel dataset for Consistency Identification in Task-oriented Dialog system. Empirical results show that state-of-the-art methods only achieve 51.3%, which is far behind the human performance of 93.2%.
  arXiv  Detail & Related papers  (2021-09-23T10:56:31Z)
• ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection [101.56529337489417]
  We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
  arXiv  Detail & Related papers  (2020-08-14T09:11:18Z)
• Symbiotic Adversarial Learning for Attribute-based Person Search [86.7506832053208]
  We present a symbiotic adversarial learning framework, called SAL.Two GANs sit at the base of the framework in a symbiotic learning scheme. Specifically, two different types of generative adversarial networks learn collaboratively throughout the training process.
  arXiv  Detail & Related papers  (2020-07-19T07:24:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.