Machine learning researcher spanning LLM tool-calling agents and vision-language spatial reasoning, with a strand in VLM interpretability
3
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-17) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Output metrics are not available in the current data snapshot: publication counts, venue distribution, and yearly production cannot be computed. This axis would measure total scholarly output and its growth over time, so no score is reported here.
Impact—
Impact data is insufficient: no citation counts, h-index, or venue-level influence figures exist in this snapshot. This axis would measure the researcher's influence on the field through citations and uptake, and it is deliberately left unscored.
Focus0/100
The focus score is 0/100, and the raw field distribution explains why: work is split exactly 50/50 between machine learning for LLM agents and for multimodal vision-language models, with interpretability as a third thread. This reads as deliberate breadth across adjacent areas rather than diffusion, though the sampled record shows no single dominant sub-field concentration.
Collaboration66/100
Bibliometrics record 26 distinct co-authors across the sampled work, yielding a collaboration score of 66/100. Author lists are multi-member and stable, with recurring names such as Harshavardhan Abichandani, Penny Chong, Jiyuan Shen, Gunraj Singh, and Ashutosh Hathidara, consistent with team-based machine learning research. Dahlmeier is last author on EDGEGEN and in middle positions on the other two papers, suggesting a senior role within the group.
Activity—
Activity data is not available in this snapshot: recent publication cadence and year-over-year output cannot be assessed. This axis would measure ongoing research tempo and engagement; the underlying record is insufficient, so no score is given.
Reproducibility55/100
Reproducibility averages 55/100 across 2 scored papers, and Evolve rates all three reviewed works as medium repro-readiness. The LSI-108K interaction-trajectory dataset supports replication of Spatial-Interactor, while EDGEGEN's small 10–15-scenario evaluations with reported judge-error rates and the attention study's small sample and manual mode partition temper straightforward re-verification.
Research summary
Daniel Dahlmeier is a machine learning researcher whose reviewed work splits evenly between two areas — LLM agents and multimodal vision-language models (50/50 field distribution) — with a further strand in VLM interpretability. The Evolve catalog holds three 2026 arXiv papers. As last author on EDGEGEN, he contributes automated synthetic edge-case generation for tool-calling agents, where Evolve reports consistent finetuning gains of +2–42% while flagging small 10–15-scenario evaluations and judge-error rates. Spatial-Interactor trains spatial reasoning through interaction with the observable physical world, using an LSI-108K interaction-trajectory dataset and SFT plus on-policy distillation, though Evolve notes its own ablation shows generic spatial-QA SFT drives most gains. A third paper, a causal perturbation study, finds visual attention faithfulness in VLMs is heterogeneous across three modes. Bibliometrics record 26 distinct co-authors (collaboration score 66/100) and a reproducibility score of 55/100 across 2 scored papers, with all three reviewed works rated medium repro-readiness. His authorship pattern — last author once, middle elsewhere, never marked corresponding — is consistent with a senior or advisory role within a multi-author group. Citation counts, h-index, publication volume, and recent activity are absent from this snapshot, so the profile should be read as an early-stage, catalog-scoped view.
Collaboration pattern
Bibliometrics record 26 distinct co-authors and a collaboration score of 66/100, with a recurring cluster — Harshavardhan Abichandani, Penny Chong, Jiyuan Shen, Gunraj Singh, Ashutosh Hathidara, Marcus Duigan Xing Yu, Jane Lo, Atin Ghosh, Yipeng Li, Kaixiang Yao, and Xu Wang — spanning both the agent and vision-language lines. Dahlmeier is last author on EDGEGEN and holds middle positions elsewhere, with no corresponding-author marks, a pattern consistent with senior supervision of group projects.
Research keywords
Highlights
Synthetic edge cases push tool-calling agents beyond happy paths
Training spatial reasoning through interaction with the physical world
Visual attention faithfulness in VLMs shown to be heterogeneous
Timeline
- 2026Last author on EDGEGEN (arXiv): synthetic edge-case generation lifts tool-agent finetuning by +2–42%, with small 10–15-scenario evaluations flagged as a limitation.
- 2026Middle author on Spatial-Interactor (arXiv): interaction-trajectory training on the LSI-108K dataset with SFT plus on-policy distillation targets VLM spatial reasoning.
- 2026Middle author on a causal perturbation study (arXiv) showing visual attention faithfulness in vision-language models is heterogeneous across three modes.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometric scores are derived from OpenAlex-based public data only; citation counts are not part of this snapshot.
- Institution, country, and ORCID are not provided; the profile is name-resolved from public bibliographic data and may conflate authors sharing the name.
- Output, impact, and activity axes lack sufficient underlying data and are left unscored.
- Reproducibility rests on only 2 scored papers, and all three reviewed works are rated medium repro-readiness.