VLDB 2026 Research / reviewers in the wild / expert
Seo Yeon Park
dblp:405/4251
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Language models and text generation · 50% Planning, search and constraint satisfaction · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › large language model reasoning
long-context reasoning |
1.0 | 1 | 2026 | PPA-Plan: Proactive Pitfall Avoidance for Reliable Planning in Long-Context LLM Reasoning · ACL (1) 2026 |
Methods — techniques the papers use, named apart from their topics
plan generation · 1.0negative constraints · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | PPA-Plan: Proactive Pitfall Avoidance for Reliable Planning in Long-Context LLM ReasoningabstractLarge language models struggle with reasoning over long contexts where relevant information is sparsely distributed.Although planand-execute frameworks mitigate this by decomposing tasks into planning and execution, their effectiveness is often limited by unreliable plan generation due to dependence on surfacelevel cues.Consequently, plans may be based on incorrect assumptions, and once a plan is formed, identifying errors and revising it reliably becomes difficult, limiting the effectiveness of reactive refinement.To address this limitation, we propose PPA-Plan, a proactive planning strategy for long-context reasoning that focuses on preventing such failures before plan generation.PPA-Plan identifies potential logical pitfalls and false assumptions, formulates them as negative constraints, and conditions plan generation on explicitly avoiding these constraints.Experiments on long-context QA benchmarks show that executing plans generated by PPA-Plan consistently outperforms existing plan-and-execute methods and direct prompting.Query: How many times did Burnett operate the claw in the passage?1. burnett_claw_operations = FIND_ELEMENT(CTX, "operate", "Burnett claw") 2. count_operations = COUNT_X(CTX, burnett_claw_operations) Answer: He directs the claw to retrieve Kriere from space.Then, he operates the claw to close around Kriere and crush him.So, the number of times Burnett operates the claw is 2. Query: Why was the murderer trying to kill Bo?⚠ Negative Constraints: -Assuming the motive for the murder is explicitly stated, rather than needing to infer it from context clues or character interactions.-Assuming that the murderer has a single, clear motive, overlooking the possibility of multiple, complex reasons for their actions.Plan ① FIND_RELATION ② FIND_ELEMENT ③ FIND_EVENT ④ CONCAT 1. murderer_relationship = FIND_RELATION(CTX,"murderer", "Bo") 2. motive_clues = FIND_ELEMENT(CTX, "motive clues", "murderer") 3. past_events = FIND_EVENT(CTX, "murderer") 4. combined_motive = CONCAT(murderer_relationship, motive_clues, past_events) Answer: …(details omitted).While the text does not provide explicit details about the murderer's motives, it suggests a broader context of violence and rivalry among individuals in the asteroid belt.Example (a) Planner Plan ① FIND_ELEMENT ② COUNT_X Executor Result 1 Result 2Query: Why was the murderer trying to kill Bo? 1. murderer_intent = FIND_INTENT(CTX, "murderer") 2. bo_relationship = FIND_RELATION(CTX, "Bo", "murderer") 3. motive = FIND_ELEMENT(CTX, "motive", "murderer") 4. ans = CONCAT(murderer_intent, bo_relationship, motive) Planner Executor Byeongjin Kim, Gyuwan Kim, Seo Yeon Park |
ACL (1) | 3 |