Pith. sign in

REVIEW 3 major objections 6 minor 68 references

This paper claims that LLM routing should be driven by a verbalized analysis of query difficulty rather than by surface similarity, and shows that a training-free router built on this idea achieves better cost-performance trade-offs than ex

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

VDAR-Router improves LLM routing by retrieving past queries with similar verbalized difficulty descriptions and ranking models by performance minus cost.

T0 review reviewed 2026-08-01 challenge →

load-bearing objection The difficulty-analysis retrieval idea is real and the routing results look good, but the reported costs omit VDAR's own analyst overhead, so the headline cost-performance claim needs a caveat. the 3 major comments →

arxiv 2607.18098 v1 pith:N2TI232U submitted 2026-07-20 cs.CL

VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval

Yu-Chien Tang , Jun-Chen Hung , Wen-Chih Peng , An-Zi Yen This is my paper
classification cs.CL
keywords LLM routingquery difficultyverbalized difficulty analysisretrieval-based routingcost-performance trade-offmodel selectiontraining-free routerRasch difficulty
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the right representation for routing a query to an LLM is not the query's surface text but a verbalized analysis of what makes it difficult. The proposed router first prompts an LLM to write a structured difficulty analysis across seven capability dimensions, embeds that analysis, retrieves historical queries with similar difficulty profiles, and then ranks candidate models by a reward that mixes retrieved performance and cost. Across three routing benchmarks, this difficulty-aware retrieval achieves better cost-performance trade-offs than query-embedding retrieval, learned routers, and IRT-based difficulty scoring, and it improves pairwise preference prediction on a 105-model leaderboard-style set. The paper supports its core assumption by showing that retrieved queries are closer in Rasch-estimated difficulty than query-embedding neighbors, and by a case study in which difficulty retrieval selects a correct oracle model while surface retrieval fails. If correct, the result implies that interpretable difficulty metadata can replace some amount of training and supervision in model selection.

Core claim

VDAR-Router's central claim is that queries that are difficult in the same way should be answered with similar relative quality by a given set of LLMs, so historical performance on difficulty-similar queries is the right evidence for routing. The paper operationalizes this by having an LLM 'Difficulty Analyst' produce a free-text analysis of the required capabilities (reasoning, comprehension, instruction following, agentic, knowledge retrieval, coding, multilingual), embedding that analysis, and retrieving the top-k most similar analyses from a training set. It then scores each candidate model by the average retrieved performance penalized by normalized cost, with the α/β weight controlling

What carries the argument

The Difficulty Analyst—a prompted LLM that writes a structured prose analysis of the capabilities required and their difficulty levels across seven dimensions (reasoning, comprehension, instruction following, agentic, knowledge retrieval, coding, multilingual)—is the central object. Its output is embedded and used for top-k retrieval over a database of historical analyses; the retrieved examples' performance records are averaged and combined with normalized cost through a weighted reward. The same analyst is also used at test time, so retrieval and evidence live in the same representation space.

Load-bearing premise

The method assumes that two queries described as equally difficult will have similar relative quality rankings across candidate models; the paper validates difficulty closeness but never directly tests that rankings transfer.

What would settle it

Collect pairs of queries whose difficulty analyses are near-identical but which require disjoint skills (for instance, one coding, one factual recall). If the Spearman correlation between model accuracy rankings within those pairs is at or below zero, then difficulty-similar retrieval is carrying no routing-relevant signal.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • A router can be deployed with no additional training: only a small language model that writes difficulty analyses and a database of past analyses and outcomes.
  • Queries that look different but are difficult in the same way are grouped together, while queries that look alike but need different capabilities are separated, so routing evidence transfers across topical boundaries.
  • The alpha/beta knob gives a direct, interpretable control over the performance-cost trade-off, so operators can tune a single routing policy instead of retraining.
  • Relative model rankings can be recovered even when supervision is only pairwise human preference, as in ArenaExpert5K, not full per-model scores.
  • Difficulty-similar neighbors stay aligned even when the training pool is halved, meaning the approach degrades gracefully with less historical data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the difficulty-analysis representation is the true carrier of the signal, then the same database should transfer across tasks and benchmarks without rebuilding, which would make routing infrastructure much cheaper to maintain.
  • A clean test of the core assumption would be to compare retrieved neighbors against neighbors selected by a multidimensional IRT model with separate ability parameters per skill dimension; if the verbal analysis adds nothing beyond the scalar Rasch difficulty, the routing gains should vanish.
  • The router inherits any blind spots of the analyst model: if the analyst systematically misjudges a category of queries, retrieval will retrieve similarly misjudged examples, so the framework's ceiling is set by the analyst's own calibration.
  • For preference-based data, adding a response-style or preference embedding alongside the difficulty analysis could fix the non-monotonic behavior as retrieval size grows, since the paper notes that difficulty-aligned neighborhoods are preference-heterogeneous.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper proposes VDAR-Router, a training-free retrieval-based LLM routing framework. For each input query, a Difficulty Analyst LLM generates a verbalized difficulty analysis over seven capability dimensions; the analysis is embedded and used to retrieve top-k historical queries with similar difficulty profiles. The router then estimates candidate-model suitability from the retrieved queries' performance and cost and selects the model maximizing a linear reward R = (1/k) Σ (α·p − β·c). The method is evaluated on RouterBench, LLMRouterBench, and ArenaExpert5K, reporting improved Reward, Pairwise Accuracy, and Spearman Correlation over KNN, RouteLLM, RouterDC, ICL-Router, and IRT-Router, together with ablations and a Rasch-based difficulty-alignment analysis.

Significance. The core idea — retrieving on verbalized difficulty analysis rather than raw query embeddings — is original and practically relevant, and the paper ships a reproducible experimental setup (seed 42, greedy decoding, public code), ablations that isolate reward ranking and difficulty retrieval, and an external Rasch check of difficulty alignment. If the reported cost-performance advantage holds end-to-end, the method would be a useful plug-and-play router. However, the central empirical claim is currently not supported as stated because the reported cost excludes the router's own inference overhead, and the mechanistic assumption that difficulty-similar queries induce similar model capability rankings is not directly validated. Both concerns are addressable with additional analysis, so the contribution has clear potential but needs substantive revision.

major comments (3)
  1. [§4.3 and Eq. (2)] The cost term in Eq. (2) and the reported Total Cost measure only 'total inference cost incurred by the selected model for each routing decision', explicitly excluding the router's own overhead. At test time VDAR-Router runs a Difficulty Analyst LLM (Gemma4-31B for RouterBench/LLMRouterBench; Qwen3.5-4B/2B for ArenaExpert5K) and an embedding model for every query. On ArenaExpert5K, Table 2 reports a selected-model Total Cost of 0.0006 for VDAR-Router (Qwen3.5-4B); a single analyst call is likely to cost more than this, so the reported Reward and Total Cost do not reflect true deployment cost. The Limitations section acknowledges additional latency but not additional cost. The authors should either include analyst+embedding cost in Total Cost, or report it separately and recompute the cost-performance comparison. This is load-bearing for the headline 'better cost-performance trade-off' cl
  2. [§5.2, §5.4, Limitations] The method's mechanism rests on the assumption, stated in §5.2 and repeated in the Limitations, that 'queries with similar difficulty profiles tend to induce similar model capability rankings.' RQ4 (Figure 5) validates only that retrieved queries have numerically closer Rasch difficulty than query-embedding or random retrieval; it never tests whether model performance rankings actually transfer from retrieved neighbors to the test query. Because RouterBench and LLMRouterBench contain full per-query performance labels for all candidate models, the authors can compute, for each test query, the true model ranking and compare it with the ranking predicted from retrieved neighbors' performance (e.g., Spearman correlation), and contrast this against query-embedding retrieval. Without such a test, the retrieved reward estimates could be misleading when two queries share difficulty but require d
  3. [Tables 1–3] All experiments appear to be a single run: one 80/20 split, temperature 0, seed 42. No error bars, confidence intervals, or significance tests are reported. Several margins are small — for example, in Table 1 LLMRouterBench at α=0.8, VDAR Reward is 42.58 vs. RouteLLM's 41.11, and at α=0.6 it is 31.14 vs. 30.34. The claim that VDAR-Router 'consistently achieves' better trade-offs would be strengthened by multiple seeds/splits or bootstrap CIs on reward differences, especially given the datasets are sampled subsets.
minor comments (6)
  1. [Eq. (2)] The phrase 'cost ... linearly normalized to [0,1]' is ambiguous. Please specify whether the normalization is per-query across candidate models, per-model across queries, or global min-max, and whether the bounds are computed on the training set. This affects the interpretation of the reported Reward values.
  2. [Table 2] The Cost column for ArenaExpert5K is not clearly labeled — state whether these are total costs across the test set or per-query averages, and specify the currency/unit (e.g., USD per 1K tokens).
  3. [Appendix B.1] The fallback price of gpt-4o-mini is used whenever an ArenaExpert5K model name cannot be mapped to OpenRouter or has no price. Report how many of the 105 candidate models rely on this fallback; if the fraction is large, the Total Cost comparisons may be skewed.
  4. [Figure 3] With six values of k plotted on the same axes, the lines are difficult to distinguish. A small-multiples layout or added error bars would improve readability; also note that the y-axis of panel (c) starts at 45, which visually exaggerates differences.
  5. [§5.3 / Appendix F] The phrase 'one of the oracle models for this query' is undefined. Clarify what 'oracle' means here (e.g., a model with the highest true performance on that query in the dataset).
  6. [Limitations] The sentence 'future research will be further explored to mitigate this test-time inference cost' is ungrammatical. More substantively, the Limitations should explicitly acknowledge that test-time analyst cost is excluded from the reported Total Cost metric, not just latency.

Circularity Check

0 steps flagged

No significant circularity: VDAR-Router is a training-free retrieval estimator validated on held-out benchmarks; the unverified difficulty-ranking assumption and excluded analyst cost are measurement issues, not definitional reductions.

full rationale

VDAR-Router's decision chain is: q → a = DifficultyAnalyst(q) (Eq. 1) → embed a → retrieve top-k training analyses → compute R(m|q) = (1/k)Σ(α·p_m(q_ir) − β·c_m(q_ir)) (Eq. 2) → select m̂ = argmax R(m|q) (Eq. 3). No routing parameter is fitted to the test query's answer or to the reported Reward; p_m and c_m are taken from the training split, and the Difficulty Analyst sees only the query text, not the selected model's response. The Rasch-difficulty analysis in Section 5.4 is an external post-hoc check: Rasch difficulties are estimated separately in Appendix D and are never used by the router, so the improved difficulty alignment of retrieved queries is an empirical observation rather than a consequence of the routing equations. The one self-citation (Tian et al., 2025, which includes authors Tang/Yen/Peng) appears in a general list of LLM capabilities in the introduction and is not load-bearing for any routing claim. The paper's own Limitations admit the unverified assumption that difficulty-similar queries induce similar model capability rankings, and the cost metric in Section 4.3 excludes router-side analyst overhead; both are correctness/measurement limitations, not circularity, because the central benchmark comparisons do not reduce to fitted inputs or to self-cited results.

Axiom & Free-Parameter Ledger

6 free parameters · 5 axioms · 0 invented entities

The method introduces no new physical or computational entities; it relies on existing LLMs and embedding models. The free parameters are mostly user-specified evaluation choices, but the cost normalization and missing-model handling are under-specified. The domain assumptions above are the real burden the central claim rests on.

free parameters (6)
  • α and β (reward trade-off weights) = 0.8/0.2 and 0.6/0.4 (user-specified)
    Equation 2 uses α·performance − β·cost with α+β=1. These are evaluation settings, not fitted to the test set, but the central 'better trade-off' claim is relative to the chosen α.
  • Retrieval size k = 30 (default)
    The paper sets k=30 for main results and analyzes k∈{5,...,30}. It is a user-chosen hyperparameter affecting reward estimates.
  • Seven capability dimensions and prompt design
    The Difficulty Analyst prompt defines seven dimensions (reasoning, comprehension, instruction following, agentic, knowledge retrieval, coding, multilingual) and a summary format. This is an ad hoc design choice that shapes the difficulty representation; no independent evidence justifies this specific set.
  • Cost normalization bounds = Unspecified
    Equation 2 says cost is 'linearly normalized to [0,1]' but does not state whether normalization is per model, per query, or global. This choice affects reward magnitudes and routing decisions.
  • Fallback price for missing ArenaExpert5K models = openai/gpt-4o-mini price
    Appendix B.1 assigns the gpt-4o-mini price to models without OpenRouter pricing, which could skew cost estimates for exotic or new models in the 105-model candidate set.
  • Rasch model priors = sigma_theta, sigma_beta unspecified
    Algorithm 1 takes prior standard deviations as inputs but the paper does not give their values; this affects the validation analysis in RQ4, not the routing itself.
axioms (5)
  • domain assumption Queries with similar difficulty profiles tend to induce similar model capability rankings.
    Stated as the core hypothesis in the Introduction and repeated in Limitations. The paper validates difficulty alignment but not ranking transfer directly.
  • domain assumption The Difficulty Analyst LLM's written difficulty analysis accurately reflects the true capability requirements of a query.
    The entire retrieval key is the output of a prompted LLM; if this analysis is noisy or biased, retrieval quality degrades. The authors acknowledge this dependency in Limitations.
  • domain assumption Embedding similarity of difficulty analyses captures difficulty similarity.
    The method uses cosine similarity in the embedding space of Qwen3-Embedding-0.6B; no evidence is given that this space aligns with difficulty, beyond the Rasch ECDF analysis.
  • domain assumption Estimated cost via input tokens and average output length of retrieved examples approximates true inference cost.
    Appendix B.3 defines the cost estimate using input token price plus average retrieved output length; this is a heuristic that may diverge from actual output lengths.
  • domain assumption Pairwise human preferences can be converted to per-model 0/1 performance scores for reward computation.
    ArenaExpert5K only has pairwise preferences; the paper converts each comparison to two model-level records with reward 1/0, implicitly treating preference as absolute correctness.

reviewed 2026-08-01 · how reviews work

0 comments
Cite this review

Pith. "Pith review of VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval." pith.science (2026). https://pith.science/paper/N2TI232U

@misc{pith2026260718098,
  author       = {Pith},
  title        = {Pith review of: VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N2TI232U}},
  note         = {Machine review of arXiv:2607.18098}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Large language models are increasingly used in practical systems, making efficient model selection important for reducing deployment cost. LLM routing has emerged as a practical solution for allocating each input query to an appropriate model under a desired cost-performance trade-off. Existing routing methods often estimate model suitability from the surface semantics or embedding similarity of the input query. However, such methods may ignore the underlying difficulty of a query, leading to suboptimal routing decisions. To address the challenge, we propose VDAR-Router, a difficulty-aware retrieval-based routing framework. For each input query, VDAR-Router first generates an explicit difficulty analysis. It then retrieves historical examples with similar difficulty profiles. Based on the retrieved records, it estimates candidate model suitability and selects the model using a reward function that considers both performance and cost. Experiments on three datasets show that VDAR-Router consistently achieves better cost-performance trade-offs than existing baselines. These results demonstrate the effectiveness of difficulty-aware retrieval for training-free LLM routing. Case studies further show that explicit query analysis helps retrieve more relevant examples and supports more reliable routing decisions.

Figures

Figures reproduced from arXiv: 2607.18098 by An-Zi Yen, Jun-Chen Hung, Wen-Chih Peng, Yu-Chien Tang.

Figure 1
Figure 1. Figure 1: An example of using difficulty analysis to [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the VDAR-Router workflow. Another line of research explores the adoption of the retrieval method to assess whether a LLM candidate is capable of answering a query. Stripelis et al. (2024) employs KNN retrieval and selects the LLM achieving the highest performance on retrieved queries. IRT-Router (Song et al., 2025) shares similar idea, and further applies Item Re￾sponse Theory (IRT) to estimate… view at source ↗
Figure 4
Figure 4. Figure 4: Case study comparing raw query retrieval [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Rasch difficulty difference between retrieved [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt used for query difficulty analysis. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

68 extracted references · 4 canonical work pages

  1. [1]

    Lingjiao Chen and Matei Zaharia and James Zou , journal=. Frugal. 2024 , url=

    2024

  2. [2]

    Dujian Ding and Ankur Mallick and Chi Wang and Robert Sim and Subhabrata Mukherjee and Victor R. Hybrid. The Twelfth International Conference on Learning Representations , year=

  3. [3]

    RouterBench: A Benchmark for Multi-

    Qitian Jason Hu and Jacob Bieker and Xiuyu Li and Nan Jiang and Benjamin Keigwin and Gaurav Ranganath and Kurt Keutzer and Shriyash Kaustubh Upadhyay , booktitle=. RouterBench: A Benchmark for Multi-. 2024 , url=

    2024

  4. [4]

    Shuhao Chen and Weisen Jiang and Baijiong Lin and James Kwok and Yu Zhang , booktitle=. Router. 2024 , url=

    2024

  5. [5]

    Gonzalez and M Waleed Kadous and Ion Stoica , booktitle=

    Isaac Ong and Amjad Almahairi and Vincent Wu and Wei-Lin Chiang and Tianhao Wu and Joseph E. Gonzalez and M Waleed Kadous and Ion Stoica , booktitle=. Route. 2025 , url=

    2025

  6. [6]

    GraphRouter: A Graph-based Router for

    Tao Feng and Yanzhen Shen and Jiaxuan You , booktitle=. GraphRouter: A Graph-based Router for. 2025 , url=

    2025

  7. [7]

    2024 , eprint=

    Cost-Effective Online Multi-LLM Selection with Versatile Reward Models , author=. 2024 , eprint=

    2024

  8. [8]

    M ix LLM : Dynamic Routing in Mixed Large Language Models

    Wang, Xinyuan and Liu, Yanchi and Cheng, Wei and Zhao, Xujiang and Chen, Zhengzhang and Yu, Wenchao and Fu, Yanjie and Chen, Haifeng. M ix LLM : Dynamic Routing in Mixed Large Language Models. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: L...

    work page doi:10.18653/v1/2025.naacl-long.545 2025

  9. [9]

    arXiv preprint arXiv:2405.20179 , year=

    Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs , author=. arXiv preprint arXiv:2405.20179 , year=

    arXiv

  10. [10]

    arXiv preprint arXiv:2507.06261 , year=

    Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities , author=. arXiv preprint arXiv:2507.06261 , year=

    Pith/arXiv arXiv

  11. [11]

    Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =

    Tian, Yong-En and Tang, Yu-Chien and Wang, Kuang-Da and Yen, An-Zi and Peng, Wen-Chih , title =. Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =. 2025 , isbn =. doi:10.1145/3726302.3730253 , abstract =

    arXiv 2025

  12. [12]

    The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

    Decompose, Analyze and Rethink: Solving Intricate Problems with Human-like Reasoning Cycle , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

  13. [13]

    Bert: Pre-training of deep bidirectional transformers for language understanding , author=. Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) , pages=

    2019

  14. [14]

    MixLLM: Dynamic Routing in Mixed Large Language Models , author=. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

    2025

  15. [15]

    Easy2Hard-Bench: Standardized Difficulty Labels for Profiling

    Mucong Ding and Chenghao Deng and Jocelyn Choo and Zichu Wu and Aakriti Agrawal and Avi Schwarzschild and Tianyi Zhou and Tom Goldstein and John Langford and Anima Anandkumar and Furong Huang , booktitle=. Easy2Hard-Bench: Standardized Difficulty Labels for Profiling. 2024 , url=

    2024

  16. [16]

    arXiv preprint arXiv:2502.11271 , year=

    Octotools: An agentic framework with extensible tools for complex reasoning , author=. arXiv preprint arXiv:2502.11271 , year=

    Pith/arXiv arXiv

  17. [17]

    arXiv preprint arXiv:2408.03314 , year=

    Scaling llm test-time compute optimally can be more effective than scaling model parameters , author=. arXiv preprint arXiv:2408.03314 , year=

    Pith/arXiv arXiv

  18. [18]

    Automating customer needs analysis: A comparative study of large language models in the travel industry , journal =

    Simone Barandoni and Lorenzo Cascone and Emiliano Marrale and Salvatore Puccio and Filippo Chiarello , keywords =. Automating customer needs analysis: A comparative study of large language models in the travel industry , journal =. 2026 , issn =. doi:https://doi.org/10.1016/j.compind.2026.104448 , url =

    arXiv 2026

  19. [19]

    2007 , publisher=

    Cognitive diagnostic assessment for education: Theory and applications , author=. 2007 , publisher=

    2007

  20. [20]

    Cogent Education , volume=

    Using reliability and item analysis to evaluate a teacher-developed test in educational measurement and evaluation , author=. Cogent Education , volume=. 2017 , publisher=

    2017

  21. [21]

    AERA Open , volume=

    Does diagnostic feedback promote learning? Evidence from a longitudinal cognitive diagnostic assessment , author=. AERA Open , volume=. 2021 , publisher=

    2021

  22. [22]

    arXiv preprint arXiv:2402.06196 , year=

    Large language models: A survey , author=. arXiv preprint arXiv:2402.06196 , year=

    Pith/arXiv arXiv

  23. [23]

    arXiv preprint arXiv:2505.16303 , year=

    INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling , author=. arXiv preprint arXiv:2505.16303 , year=

    Pith/arXiv arXiv

  24. [24]

    arXiv preprint arXiv:2503.05641 , year=

    Symbolic mixture-of-experts: Adaptive skill-based routing for heterogeneous reasoning , author=. arXiv preprint arXiv:2503.05641 , year=

    Pith/arXiv arXiv

  25. [25]

    MEXA : Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation

    Yu, Shoubin and Zhang, Yue and Wang, Ziyang and Yoon, Jaehong and Bansal, Mohit. MEXA : Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025. doi:10.18653/v1/2025.findings-emnlp.1233

    work page doi:10.18653/v1/2025.findings-emnlp.1233 2025

  26. [26]

    Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

    Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models , author=. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

    2024

  27. [27]

    Richard Zhuang and Tianhao Wu and Zhaojin Wen and Andrew Li and Jiantao Jiao and Kannan Ramchandran , booktitle=. Embed. 2025 , url=

    2025

  28. [28]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Llm-planner: Few-shot grounded planning for embodied agents with large language models , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  29. [29]

    Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View , author=. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  30. [30]

    Qwen2.5: A Party of Foundation Models , url =

    Qwen Team , month =. Qwen2.5: A Party of Foundation Models , url =

  31. [31]

    2024 , url=

    GPT-4o mini: advancing cost-efficient intelligence , author=. 2024 , url=

    2024

  32. [32]

    2025 , url=

    Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities , author=. 2025 , url=

    2025

  33. [33]

    arXiv preprint arXiv:2506.05176 , year=

    Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models , author=. arXiv preprint arXiv:2506.05176 , year=

    Pith/arXiv arXiv

  34. [34]

    IRT -Router: Effective and Interpretable Multi- LLM Routing via Item Response Theory

    Song, Wei and Huang, Zhenya and Cheng, Cheng and Gao, Weibo and Xu, Bihan and Zhao, GuanHao and Wang, Fei and Wu, Runze. IRT -Router: Effective and Interpretable Multi- LLM Routing via Item Response Theory. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025. doi:10.18653/v1/2025.acl-long.761

    doi:10.18653/v1/2025.acl-long.761 2025

  35. [35]

    arXiv preprint arXiv:2503.11074 , year=

    Exploring the Necessity of Reasoning in LLM-based Agent Scenarios , author=. arXiv preprint arXiv:2503.11074 , year=

    Pith/arXiv arXiv

  36. [36]

    arXiv preprint arXiv:2503.17979 , year=

    Trade-offs in large reasoning models: An empirical analysis of deliberative and adaptive reasoning over foundational capabilities , author=. arXiv preprint arXiv:2503.17979 , year=

    arXiv

  37. [37]

    Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=

    HellaSwag: Can a Machine Really Finish Your Sentence? , author=. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=

  38. [38]

    arXiv preprint arXiv:1803.05457 , year=

    Think you have solved question answering? try arc, the ai2 reasoning challenge , author=. arXiv preprint arXiv:1803.05457 , year=

    Pith/arXiv arXiv

  39. [39]

    arXiv preprint arXiv:2601.07206 , year =

    LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing , author =. arXiv preprint arXiv:2601.07206 , year =

    arXiv

  40. [40]

    T ensor O pera Router: A Multi-Model Router for Efficient LLM Inference

    Stripelis, Dimitris and Xu, Zhaozhuo and Hu, Zijian and Shah, Alay Dilipbhai and Jin, Han and Yao, Yuhang and Zhang, Jipeng and Zhang, Tong and Avestimehr, Salman and He, Chaoyang. T ensor O pera Router: A Multi-Model Router for Efficient LLM Inference. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track....

    doi:10.18653/v1/2024.emnlp-industry.34 2024

  41. [41]

    Proceedings of the AAAI Conference on Artificial Intelligence , author=

    ICL-Router: In-Context Learned Model Representations for LLM Routing , volume=. Proceedings of the AAAI Conference on Artificial Intelligence , author=. 2026 , month=. doi:10.1609/aaai.v40i39.40628 , abstractNote=

    work page doi:10.1609/aaai.v40i39.40628 2026

  42. [42]

    , author=

    Probabilistic models for some intelligence and attainment tests. , author=. 1993 , publisher=

    1993

  43. [43]

    2025 , publisher =

    arena-expert-5k , author =. 2025 , publisher =

    2025

  44. [44]

    2026 , howpublished =

    Gemma 4 Model Card , author =. 2026 , howpublished =

    2026

  45. [45]

    2026 , month = apr, day =

    Gemma 4: Byte for Byte, the Most Capable Open Models , author =. 2026 , month = apr, day =

    2026

  46. [46]

    The American Journal of Psychology , volume=

    The proof and measurement of association between two things , author=. The American Journal of Psychology , volume=. 1904 , publisher=

    1904

  47. [47]

    2025 , howpublished =

    LLMRouter: An Open-Source Library for LLM Routing , author =. 2025 , howpublished =

    2025

  48. [48]

    The Eleventh International Conference on Learning Representations , year=

    ReAct: Synergizing Reasoning and Acting in Language Models , author=. The Eleventh International Conference on Learning Representations , year=

  49. [49]

    Thirty-seventh Conference on Neural Information Processing Systems , year=

    Toolformer: Language Models Can Teach Themselves to Use Tools , author=. Thirty-seventh Conference on Neural Information Processing Systems , year=

  50. [50]

    ACM Comput

    Qin, Yujia and Hu, Shengding and Lin, Yankai and Chen, Weize and Ding, Ning and Cui, Ganqu and Zeng, Zheni and Zhou, Xuanhe and Huang, Yufei and Xiao, Chaojun and Han, Chi and Fung, Yi Ren and Su, Yusheng and Wang, Huadong and Qian, Cheng and Tian, Runchu and Zhu, Kunlun and Liang, Shihao and Shen, Xingyu and Xu, Bokai and Zhang, Zhen and Ye, Yining and L...

    2024

  51. [51]

    O pen C ode I nterpreter: Integrating Code Generation with Execution and Refinement

    Zheng, Tianyu and Zhang, Ge and Shen, Tianhao and Liu, Xueling and Lin, Bill Yuchen and Fu, Jie and Chen, Wenhu and Yue, Xiang. O pen C ode I nterpreter: Integrating Code Generation with Execution and Refinement. Findings of the Association for Computational Linguistics: ACL 2024. 2024. doi:10.18653/v1/2024.findings-acl.762

    doi:10.18653/v1/2024.findings-acl.762 2024

  52. [52]

    Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , url =

    Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and K\". Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , url =. Advances in Neural Information Processing Systems , editor =

  53. [53]

    2026 , eprint=

    OpenAI GPT-5 System Card , author=. 2026 , eprint=

    2026

  54. [54]

    2024 , url=

    Yubo Wang and Xueguang Ma and Ge Zhang and Yuansheng Ni and Abhranil Chandra and Shiguang Guo and Weiming Ren and Aaran Arulraj and Xuan He and Ziyan Jiang and Tianle Li and Max Ku and Kai Wang and Alex Zhuang and Rongqi Fan and Xiang Yue and Wenhu Chen , booktitle=. 2024 , url=

    2024

  55. [55]

    Edward J Hu and yelong shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen , booktitle=. Lo. 2022 , url=

    2022

  56. [56]

    2025 , eprint=

    Qwen3 Technical Report , author=. 2025 , eprint=

    2025

  57. [57]

    SMART : Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction

    Scarlatos, Alexander and Fernandez, Nigel and Ormerod, Christopher and Lottridge, Susan and Lan, Andrew. SMART : Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025. doi:10.18653/v1/2025.emnlp-main.1274

    doi:10.18653/v1/2025.emnlp-main.1274 2025

  58. [58]

    Proceedings of the ACM on Web Conference 2025 , pages =

    Zhou, Yiyun and Lv, Zheqi and Zhang, Shengyu and Chen, Jingyuan , title =. Proceedings of the ACM on Web Conference 2025 , pages =. 2025 , isbn =. doi:10.1145/3696410.3714607 , abstract =

    arXiv 2025

  59. [59]

    Rossi and Andrew Lan and Zichao Wang , booktitle=

    Nigel Fernandez and Branislav Kveton and Ryan A. Rossi and Andrew Lan and Zichao Wang , booktitle=. 2026 , url=

    2026

  60. [60]

    tinyBenchmarks: evaluating

    Felipe Maia Polo and Lucas Weber and Leshem Choshen and Yuekai Sun and Gongjun Xu and Mikhail Yurochkin , booktitle=. tinyBenchmarks: evaluating. 2024 , url=

    2024

  61. [61]

    Dense Passage Retrieval for Open-Domain Question Answering

    Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau. Dense Passage Retrieval for Open-Domain Question Answering. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. doi:10.18653/v1/2020.emnlp-main.550

    doi:10.18653/v1/2020.emnlp-main.550 2020

  62. [62]

    Gonzalez and Ion Stoica , booktitle=

    Wei-Lin Chiang and Lianmin Zheng and Ying Sheng and Anastasios Nikolas Angelopoulos and Tianle Li and Dacheng Li and Banghua Zhu and Hao Zhang and Michael Jordan and Joseph E. Gonzalez and Ion Stoica , booktitle=. Chatbot Arena: An Open Platform for Evaluating. 2024 , url=

    2024

  63. [63]

    Gonzalez and Ion Stoica , booktitle=

    Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zi Lin and Zhuohan Li and Dacheng Li and Eric Xing and Hao Zhang and Joseph E. Gonzalez and Ion Stoica , booktitle=. Judging. 2023 , url=

    2023

  64. [64]

    Universal Model Routing for Efficient

    Wittawat Jitkrittum and Harikrishna Narasimhan and Ankit Singh Rawat and Jeevesh Juneja and Congchao Wang and Zifeng Wang and Alec Go and Chen-Yu Lee and Pradeep Shenoy and Rina Panigrahy and Aditya Krishna Menon and Sanjiv Kumar , booktitle=. Universal Model Routing for Efficient. 2026 , url=

    2026

  65. [65]

    Generalising LLM Routing using Past Performance Retrieval: A Few-Shot Router is Sufficient

    Varangot-Reille, Clovis and Bouvard, Christophe and Gourru, Antoine. Generalising LLM Routing using Past Performance Retrieval: A Few-Shot Router is Sufficient. Proceedings of the 19th Conference of the E uropean Chapter of the A ssociation for C omputational L inguistics (Volume 4: Student Research Workshop). 2026. doi:10.18653/v1/2026.eacl-srw.22

    work page doi:10.18653/v1/2026.eacl-srw.22 2026

  66. [66]

    Enhancing Retrieval-Augmented Generation: A Study of Best Practices

    Li, Siran and Stenzel, Linus and Eickhoff, Carsten and Bahrainian, Seyed Ali. Enhancing Retrieval-Augmented Generation: A Study of Best Practices. Proceedings of the 31st International Conference on Computational Linguistics. 2025

    2025

  67. [67]

    PersonalizedRouter: Personalized

    Zhongjie Dai and Tao Feng and Jiaxuan You , journal=. PersonalizedRouter: Personalized. 2025 , url=

    2025

  68. [68]

    arXiv preprint arXiv:2506.16655 , year=

    Arch-Router: Aligning LLM Routing with Human Preferences , author=. arXiv preprint arXiv:2506.16655 , year=

    Pith/arXiv arXiv

This paper was first reviewed by deepseek-v4-flash on August 1, 2026.