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Paper Citation Record · LEDGER

Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2410.04707.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.04707 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:18:47.988297Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T22:26:17.063908Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a5a8c85f-007f-4312-b530-284b87c955f2 · inbound

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs cites this paper.

Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-13T15:51:29.476618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T15:51:29.022336Z digest=sha256:feeeef74ed2380a32c49d16433fad6186d3e327be200f3ebcaeaf6f084d03eb1

Observation 45a5734c-5395-4298-b185-f158ee9e72dd · inbound

Emergent Response Planning in LLMs cites this paper.

Emergent Response Planning in LLMs Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-08T16:18:47.988297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:18:47.988297Z digest=sha256:df2baa132cc793d829be67bd9ebd7ef7482eca5abe2cc7ec948f8b64e9465139

Observation 15aea0aa-d327-40a5-b8ae-0cf900157896 · inbound

EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning cites this paper.

EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T15:06:00.664496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:06:00.664496Z digest=sha256:f04cd3bf4bff39a68e8a20ba8e06ae3dabb99a6e64e6bcc8e3911e6bde5a7b1a

Observation 77aebce6-4914-4c51-956f-81cdfa8e45c0 · inbound

Structured Pruning for Diverse Best-of-N Reasoning Optimization cites this paper.

Structured Pruning for Diverse Best-of-N Reasoning Optimization Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T10:56:23.551437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:56:23.551437Z digest=sha256:cb399887f52dbc5737ae3a2322e217e63495b59fe3224f31ac15d7aa2c95b695

Observation 3cb1a58f-a184-4b77-b97a-e4ffabe2b42f · inbound

BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens cites this paper.

BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-05T17:04:38.139893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:04:38.139893Z digest=sha256:bda5c81d8ed7bc0ca67dece874f4b3ea7e326a65a66caf6f3b91f2fa750c3b51

Observation ef116e5c-cec4-4023-acfd-1c09196c69b0 · inbound

Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation cites this paper.

Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-05T05:34:40.267369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:34:40.267369Z digest=sha256:a92ac5db905f78f3f0ca3387b5ebdf1e4b78eb3340bf1571937ab008ade84b2c

Observation f25098e2-7bad-4ba5-976b-82c4cdecf6a8 · inbound

From Long to Short: LLMs Excel at Trimming Own Reasoning Chains cites this paper.

From Long to Short: LLMs Excel at Trimming Own Reasoning Chains Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T00:03:58.401997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T00:03:58.401997Z digest=sha256:ee4f11ee5bfba5a54a4e3e24e620b6236ca829b3bb34bb709d2693854cff2584

Observation c091b011-5038-4d53-802e-73825271e40e · inbound

Latency and Token-Aware Test-Time Compute cites this paper.

Latency and Token-Aware Test-Time Compute Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-04T18:37:58.927864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T18:37:58.927864Z digest=sha256:b360def509a82cce452db3c51baf4a44b344a44df9e867ad639165486a20ec57

Observation 1b269d24-e216-4ceb-aed9-2421a09ac982 · inbound

GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts cites this paper.

GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-16T15:58:04.004882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T15:53:07.496412Z digest=sha256:7f1fa29491fc7194e2d2aa5a4d0c8a688eca4c30ec10b581970f8b8aa511fcd0

Observation 1f7ddd34-2dea-4a48-8a55-c14c8196e823 · inbound

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models cites this paper.

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-21T14:10:13.026482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T14:09:26.842696Z digest=sha256:078320ac11ff0414ad2a04502aa8118a663288bcd9fede009e5b34fc73e0dba3

Observation df8e1a92-8e47-45ef-a3d8-142108e3ae33 · inbound

Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents cites this paper.

Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T12:40:08.825784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T12:35:23.879207Z digest=sha256:31e8214d3bae8a560dbcdc139796a06e8286c4f58fc5b8f3587c1ab45034ea3b

Observation 663b5e4e-27be-4e6c-a5e7-6c69885c12a4 · inbound

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models cites this paper.

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 113

Resolution
verified exact
arxiv_id, observed 2026-05-20T19:53:42.214972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T19:53:04.689519Z digest=sha256:3f9105c6b1866577a7b71d1978b9ebebb72aab4444c1db534916f6cacb99f10c

Observation 3fae7f65-7cfe-421a-a7de-141aa2273200 · inbound

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models cites this paper.

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-06-30T19:45:00.805465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-30T19:40:41.219923Z digest=sha256:1b8f49e4f2b400aadcf45f7a170a617c7480dcacc0bd230b5c0f9f80162e0bbc

Observation 890de3f6-cd4e-445c-93a4-716a155a044a · inbound

Self-Supervised On-Policy Distillation for Reasoning Language Models cites this paper.

Self-Supervised On-Policy Distillation for Reasoning Language Models Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 66

Resolution
verified exact
arxiv_id, observed 2026-05-20T14:43:22.181773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T14:42:55.368104Z digest=sha256:38006765a19840159001ef3a1d8e940aa70cd704ff03fb371474a2043be14718

Observation e49d0f63-5f9b-414f-a8f1-f7ddcb255eb9 · inbound

ATLAS: Agentic Test-time Learning-to-Allocate Scaling cites this paper.

ATLAS: Agentic Test-time Learning-to-Allocate Scaling Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:26:17.065605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T15:27:28.290178Z digest=sha256:b070eee45488ffdcdcaa2eda6eef4a47c64e0f540b3aa1e887aafaad923e25a0

Observation 393c9c4a-8dd6-43ac-b9ce-b9055cce5f6a · inbound

Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models cites this paper.

Falsification, Not Exposure: An Internally Preregistered Placebo-Controlled Decomposition of Self-Repair Feedback in Frozen Small Code Models Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-01T11:05:42.264953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-01T04:44:56.520156Z digest=sha256:db5d57505b349ed4e03e5eb0ef6201555dc34f7f3551a2fff533396738761788