Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling

Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/training-in-context-and-in-weights-mixtures-via-contrastive-context-sampling

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID training-in-context-and-in-weights-mixtures-via-contrastive-context-sampling | Route /signal-canvas/training-in-context-and-in-weights-mixtures-via-contrastive-context-sampling

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/training-in-context-and-in-weights-mixtures-via-contrastive-context-sampling

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "training-in-context-and-in-weights-mixtures-via-contrastive-context-sampling",
    "query_text": "Summarize Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling",
  "normalized_query": "2604.01601",
  "route": "/signal-canvas/training-in-context-and-in-weights-mixtures-via-contrastive-context-sampling",
  "paper_ref": "training-in-context-and-in-weights-mixtures-via-contrastive-context-sampling",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling

PDF: https://arxiv.org/pdf/2604.01601v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:41.059Z

Signal Canvas receipt window

Not build-ready: Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling

/buildability/training-in-context-and-in-weights-mixtures-via-contrastive-context-sampling

Ignoreblocked

Subject: Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
In this paper we show that the similarity structure between target inputs and context examples also plays an important role.
Implicationpartial
Directly stated in abstract as a key finding of the paper
Verificationpartial
partial
Evidencepartial
Random context leads to loss of ICL and IWL dominance
Implicationpartial
Directly stated in abstract with clear causal relationship
Verificationpartial
partial
Evidencepartial
only similar examples in context causes ICL to degenerate to copying labels without regard to relevance
Implicationpartial
Directly stated in abstract with clear causal relationship
Verificationpartial
partial
Evidencepartial
we propose a simple Contrastive-Context which enforces two types of contrasts: (1) mix of similar and random examples within a context to evolve a correct form of ICL, and (2) varying grades of similarity across contexts to evolve ICL-IWL mixtures.
Implicationpartial
Directly stated in abstract as the proposed method
Verificationpartial
partial
Evidencepartial
Diagnostic probes confirm that contrasted contexts yield stable ICL-IWL mixtures, avoiding collapse into pure ICL, IWL, or copying.
Implicationpartial
Directly stated in abstract as a key result of the method
Verificationpartial
partial
Evidencepartial
standard task-specific fine-tuning often erodes ICL
Implicationpartial
Directly stated in abstract as motivation for the work
Verificationpartial
partial
Evidencepartial
Prior work has shown that emergence of ICL after IC-Train depends on factors such as task diversity and training duration.
Implicationpartial
Directly stated in abstract as established prior knowledge
Verificationpartial
partial
Evidencepartial
We validate with extensive empirical evaluation on four LLMs and several tasks.
Implicationpartial
Directly stated in abstract as part of the methodology
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.

Training In-Context and In-Weights Mixtures Via Contrastive Context Sampling

Use Signal Canvas as the narrative proof surface

Use this Signal Canvas via API or MCP

Signal Canvas proof surface