HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs

HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/hestia-a-hessian-guided-differentiable-quantization-aware-training-framework-for-extremely-low-bit-llms

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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 hestia-a-hessian-guided-differentiable-quantization-aware-training-framework-for-extremely-low-bit-llms | Route /signal-canvas/hestia-a-hessian-guided-differentiable-quantization-aware-training-framework-for-extremely-low-bit-llms

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/hestia-a-hessian-guided-differentiable-quantization-aware-training-framework-for-extremely-low-bit-llms

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 0

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs

PDF: https://arxiv.org/pdf/2601.20745v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Signal Canvas receipt window

Watch and verify: HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs

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Subject: HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs

Verdict

Watch

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

HESTIA: A Hessian-Guided Differentiable Quantization-Aware Training Framework for Extremely Low-Bit LLMs

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