Learning Personalized Agents from Human Feedback

Learning Personalized Agents from Human Feedback | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/learning-personalized-agents-from-human-feedback

degraded

Proof freshness: stale
Proof status: failed
Display score: 9/10
Last proof check: 2026-03-17
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page has proof data, but the latest verification did not complete cleanly.

Agent Handoff

Canonical ID learning-personalized-agents-from-human-feedback | Route /signal-canvas/learning-personalized-agents-from-human-feedback

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/learning-personalized-agents-from-human-feedback

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "learning-personalized-agents-from-human-feedback",
    "query_text": "Summarize Learning Personalized Agents from Human Feedback"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Learning Personalized Agents from Human Feedback",
  "normalized_query": "2602.16173",
  "route": "/signal-canvas/learning-personalized-agents-from-human-feedback",
  "paper_ref": "learning-personalized-agents-from-human-feedback",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: degraded

Claims: 12

References: Pending verification

Proof: Verification pending

Freshness state: stale

Source paper: Learning Personalized Agents from Human Feedback

PDF: https://arxiv.org/pdf/2602.16173v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-17T19:46:04.153Z

Signal Canvas receipt window

Watch and verify: Learning Personalized Agents from Human Feedback

/buildability/learning-personalized-agents-from-human-feedback

Watchwatch

Subject: Learning Personalized Agents from Human Feedback

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 11Mixed 1Weak 0

Evidencepartial
We introduce Personalized Agents from Human Feedback (PAHF), a framework for continual personalization in which agents learn online from live interaction using explicit per-user memory.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
PAHF operationalizes a three-step loop: (1) seeking pre-action clarification to resolve ambiguity, (2) grounding actions in preferences retrieved from memory, and (3) integrating post-action feedback to update memory when preferences drift.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
To evaluate this capability, we develop a four-phase protocol and two benchmarks in embodied manipulation and online shopping.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
PAHF learns substantially faster and consistently outperforms both no-memory and single-channel baselines
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
PAHF learns substantially faster and consistently outperforms both no-memory and single-channel baselines, reducing initial personalization error
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
PAHF learns substantially faster and consistently outperforms both no-memory and single-channel baselines, enabling rapid adaptation to preference shifts.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencemissing
Evidence not extracted yet.
Implicationmissing
Implication not extracted yet.
Verificationmissing
missing
Evidencemissing
Evidence not extracted yet.
Implicationmissing
Implication not extracted yet.
Verificationmissing
missing
Evidencepartial
PAHF operationalizes a three-step loop: (1) seeking pre-action clarification to resolve ambiguity, (2) grounding actions in preferences retrieved from memory, and (3) integrating post-action feedback to update memory when preferences drift.
Implicationpartial
Directly described in the abstract and method overview.
Verificationpartial
partial
Evidencepartial
PAHF learns substantially faster and consistently outperforms both no-memory and single-channel baselines, reducing initial personalization error and enabling rapid adaptation to preference shifts.
Implicationpartial
Explicitly stated in the abstract with comparative performance claims.
Verificationpartial
partial
Evidencepartial
To evaluate this capability, we develop a four-phase protocol and two benchmarks in embodied manipulation and online shopping.
Implicationpartial
Directly stated in the abstract as part of the evaluation protocol.
Verificationpartial
partial
Evidencepartial
reducing initial personalization error and enabling rapid adaptation to preference shifts.
Implicationpartial
Explicitly claimed in the abstract, though exact error reduction numbers are not provided in the excerpt.
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Learning Personalized Agents from Human Feedback

Use Signal Canvas as the narrative proof surface

Use this Signal Canvas via API or MCP

Signal Canvas proof surface